mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-19 22:39:03 +00:00
30ab1d6a71
* Multi ABD - initial commit * Clang-foramt fix * block gemm, unify the name of CDataType * Apply chnages to mem-pipeline * Rollback prefix for DType and Layout * Gemm Kernel Basic, rename * WMMA config * Grouped GEMM * Clang-format * Dropout, name * Review v2 * Move element_wise fn to unnary, remov old ones fn * clang-format * Fix issue review * WP operator adjust to universal gemm * v2 prepare * Remove unused comment * Remove vectorsize * Rollback * Adjust pipeline for abd * Shuffle argument * CI-fail fix quant * Fix ag_br pipeline * Failing tests * Typo * Single argument support
529 lines
20 KiB
C++
529 lines
20 KiB
C++
// 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 QDataType,
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typename BDataType,
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typename AccDataType,
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typename CDataType,
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uint32_t QuantGroupSize,
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bool aquant,
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typename AElementOp = ck_tile::identity,
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typename BElementOp = ck_tile::identity,
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typename ACCElementOp = ck_tile::identity>
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CK_TILE_HOST void reference_gemm_quant(const HostTensor<ADataType>& a_m_k,
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const HostTensor<QDataType>& q,
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const HostTensor<BDataType>& b_k_n,
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HostTensor<CDataType>& c_m_n,
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const AElementOp& a_element_op = {},
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const BElementOp& b_element_op = {},
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const ACCElementOp& acc_element_op = {})
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{
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const std::size_t M = a_m_k.get_length(0);
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const std::size_t N = b_k_n.get_length(1);
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const std::size_t K = a_m_k.get_length(1);
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auto f_mn = [&](auto m, auto n) {
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AccDataType v_acc = 0, v_block_acc = 0;
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static_assert(std::is_same_v<ADataType, pk_int4_t> || std::is_same_v<ADataType, fp8_t> ||
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std::is_same_v<ADataType, bf8_t>);
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static_assert(std::is_same_v<BDataType, fp8_t> || std::is_same_v<BDataType, bf8_t> ||
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std::is_same_v<BDataType, pk_int4_t>);
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static_assert(std::is_same_v<AccDataType, float>);
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static_assert(std::is_same_v<CDataType, float> ||
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std::is_same_v<CDataType, ck_tile::half_t>);
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for(std::size_t k = 0; k < K; ++k)
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{
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AccDataType v_a;
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AccDataType v_b;
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if constexpr(std::is_same_v<ADataType, pk_int4_t>)
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{
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const pk_int4_t pk_val = a_element_op(a_m_k(m, k));
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const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(pk_val);
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if(k % 2 == 1)
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v_a = fp32_val.hi;
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else
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v_a = fp32_val.lo;
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}
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else
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{
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v_a = ck_tile::type_convert<AccDataType>(a_element_op(a_m_k(m, k)));
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}
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if constexpr(std::is_same_v<BDataType, pk_int4_t>)
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{
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const pk_int4_t pk_val = b_element_op(b_k_n(k, n));
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const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(pk_val);
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if(k % 2 == 1)
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v_b = fp32_val.hi;
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else
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v_b = fp32_val.lo;
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}
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else if constexpr(std::is_same_v<BDataType, fp8_t>)
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{
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v_b = fp8_to_float_raw(b_element_op(b_k_n(k, n)));
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}
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else
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{
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v_b = ck_tile::type_convert<AccDataType>(b_element_op(b_k_n(k, n)));
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}
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v_block_acc += v_a * v_b;
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// Apply group dequant scale
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if((k + 1) % QuantGroupSize == 0)
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{
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float scale = 0.f;
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index_t outer_dim = (aquant) ? m : k / QuantGroupSize;
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index_t inner_dim = (aquant) ? k / QuantGroupSize : n;
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if constexpr(std::is_same_v<QDataType, float>)
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{
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scale = q(outer_dim, inner_dim);
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}
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else if constexpr(std::is_same_v<QDataType, ck_tile::fp8_t>)
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{
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scale = fp8_to_float_raw(q(outer_dim, inner_dim));
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}
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else if constexpr(std::is_same_v<QDataType, ck_tile::bf8_t>)
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{
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scale = bf8_to_float_raw(q(outer_dim, inner_dim));
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}
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else
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{
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static_assert(false, "Unexpected Q datatype.");
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}
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v_block_acc *= scale;
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v_acc += v_block_acc;
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v_block_acc = 0;
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}
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}
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c_m_n(m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
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};
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make_ParallelTensorFunctor(f_mn, M, N)(std::thread::hardware_concurrency());
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std::cout << std::endl;
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}
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template <typename ADataType,
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typename AQDataType,
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typename BDataType,
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typename BQDataType,
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typename AccDataType,
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typename CDataType,
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typename AElementOp = ck_tile::identity,
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typename BElementOp = ck_tile::identity,
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typename ACCElementOp = ck_tile::identity>
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CK_TILE_HOST void reference_gemm_rowcol_quant(const HostTensor<ADataType>& a_m_k,
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const HostTensor<AQDataType>& aq_m_1,
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const HostTensor<BDataType>& b_k_n,
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const HostTensor<BQDataType>& bq_1_n,
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HostTensor<CDataType>& c_m_n,
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const AElementOp& a_element_op = {},
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const BElementOp& b_element_op = {},
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const ACCElementOp& acc_element_op = {})
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{
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static_assert(std::is_same_v<ADataType, fp8_t> || std::is_same_v<ADataType, bf8_t>);
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static_assert(std::is_same_v<BDataType, fp8_t> || std::is_same_v<BDataType, bf8_t>);
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static_assert(std::is_same_v<AccDataType, float>);
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static_assert(std::is_same_v<CDataType, float> || std::is_same_v<CDataType, ck_tile::half_t>);
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static_assert(std::is_same_v<AQDataType, float> && std::is_same_v<BQDataType, float>);
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const std::size_t M = a_m_k.get_length(0);
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const std::size_t N = b_k_n.get_length(1);
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const std::size_t K = a_m_k.get_length(1);
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auto f_mn = [&](auto m, auto n) {
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// Init accumulator
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AccDataType v_acc = 0;
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// Get row scale for A and column scale for B
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float a_scale = aq_m_1(m, 0);
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float b_scale = bq_1_n(0, n);
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// Compute the dot product
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for(std::size_t k = 0; k < K; ++k)
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{
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AccDataType v_a;
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AccDataType v_b;
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// Process A data
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if constexpr(std::is_same_v<ADataType, pk_int4_t>)
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{
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const pk_int4_t pk_val = a_element_op(a_m_k(m, k));
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const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t_signed_conversion(pk_val);
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if(k % 2 == 1)
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v_a = fp32_val.hi;
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else
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v_a = fp32_val.lo;
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}
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else
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{
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v_a = ck_tile::type_convert<AccDataType>(a_element_op(a_m_k(m, k)));
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}
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// Process B data
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if constexpr(std::is_same_v<BDataType, pk_int4_t>)
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{
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const pk_int4_t pk_val = b_element_op(b_k_n(k, n));
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const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t_signed_conversion(pk_val);
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if(k % 2 == 1)
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v_b = fp32_val.hi;
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else
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v_b = fp32_val.lo;
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}
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else if constexpr(std::is_same_v<BDataType, fp8_t>)
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{
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v_b = fp8_to_float_raw(b_element_op(b_k_n(k, n)));
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}
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else
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{
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v_b = ck_tile::type_convert<AccDataType>(b_element_op(b_k_n(k, n)));
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}
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v_acc += v_a * v_b;
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}
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v_acc = v_acc * a_scale * b_scale;
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c_m_n(m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
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};
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make_ParallelTensorFunctor(f_mn, M, N)(std::thread::hardware_concurrency());
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std::cout << std::endl;
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}
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template <typename ADataType,
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typename BDataType,
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typename AccDataType,
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typename CDataType,
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typename AElementOp = ck_tile::identity,
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typename BElementOp = ck_tile::identity,
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typename ACCElementOp = ck_tile::identity>
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CK_TILE_HOST void reference_gemm(const HostTensor<ADataType>& a_m_k,
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const HostTensor<BDataType>& b_k_n,
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HostTensor<CDataType>& c_m_n,
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const AElementOp& a_element_op = {},
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const BElementOp& b_element_op = {},
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const ACCElementOp& acc_element_op = {})
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{
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const std::size_t M = a_m_k.get_length(0);
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const std::size_t N = b_k_n.get_length(1);
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const std::size_t K = a_m_k.get_length(1);
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auto f_mn = [&](auto m, auto n) {
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AccDataType v_acc = 0;
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for(std::size_t k = 0; k < K; ++k)
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{
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AccDataType v_a;
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AccDataType v_b;
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if constexpr(std::is_same_v<ADataType, pk_int4_t>)
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{
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const pk_int4_t pk_val = a_element_op(a_m_k(m, k));
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const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(pk_val);
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if(k % 2 == 1)
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v_a = fp32_val.hi;
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else
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v_a = fp32_val.lo;
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}
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else
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{
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v_a = ck_tile::type_convert<AccDataType>(a_element_op(a_m_k(m, k)));
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}
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if constexpr(std::is_same_v<BDataType, pk_int4_t>)
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{
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const pk_int4_t pk_val = b_element_op(b_k_n(k, n));
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const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(pk_val);
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if(k % 2 == 1)
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v_b = fp32_val.hi;
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else
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v_b = fp32_val.lo;
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}
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else
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{
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v_b = ck_tile::type_convert<AccDataType>(b_element_op(b_k_n(k, n)));
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}
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v_acc += v_a * v_b;
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}
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c_m_n(m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
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};
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make_ParallelTensorFunctor(f_mn, M, N)(std::thread::hardware_concurrency());
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}
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template <typename AsDataType,
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typename BsDataType,
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typename DsDataType,
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typename AccDataType,
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typename CDataType,
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typename AElementOp,
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typename BElementOp,
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typename CDElementOp,
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typename ADataType = remove_cvref_t<std::tuple_element_t<0, AsDataType>>,
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typename BDataType = remove_cvref_t<std::tuple_element_t<0, BsDataType>>,
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typename DDataType = remove_cvref_t<std::tuple_element_t<0, DsDataType>>>
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CK_TILE_HOST void
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reference_gemm_multiple_abd(const std::array<HostTensor<ADataType>, AsDataType::size()>& as_m_k,
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const std::array<HostTensor<BDataType>, BsDataType::size()>& bs_k_n,
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const std::array<HostTensor<DDataType>, DsDataType::size()>& ds_m_n,
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HostTensor<ADataType>& a_m_k,
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HostTensor<BDataType>& b_k_n,
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HostTensor<CDataType>& c_m_n,
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const AElementOp& a_element_op = {},
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const BElementOp& b_element_op = {},
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const CDElementOp& acc_element_op = {})
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{
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const std::size_t M = a_m_k.get_length(0);
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const std::size_t N = b_k_n.get_length(1);
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const std::size_t K = a_m_k.get_length(1);
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auto as_m_k_tuple =
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generate_tie([&](auto idx) -> auto& { return as_m_k[idx]; }, number<AsDataType::size()>{});
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auto bs_k_n_tuple =
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generate_tie([&](auto idx) -> auto& { return bs_k_n[idx]; }, number<BsDataType::size()>{});
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auto ds_m_n_tuple =
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generate_tie([&](auto idx) -> auto& { return ds_m_n[idx]; }, number<DsDataType::size()>{});
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// Apply elementwise function to A
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auto a_elementwise_fn = [&](auto i, auto j) {
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ck_tile::apply([&](auto&&... t) { a_element_op(a_m_k(i, j), t(i, j)...); }, as_m_k_tuple);
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};
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make_ParallelTensorFunctor(a_elementwise_fn, M, K)(std::thread::hardware_concurrency());
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// Apply elementwise function to B
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auto b_elementwise_fn = [&](auto i, auto j) {
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ck_tile::apply([&](auto&&... t) { b_element_op(b_k_n(i, j), t(i, j)...); }, bs_k_n_tuple);
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};
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make_ParallelTensorFunctor(b_elementwise_fn, K, N)(std::thread::hardware_concurrency());
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auto f_mk_kn_mn = [&](auto m, auto n) {
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AccDataType v_acc = 0;
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for(std::size_t k = 0; k < K; ++k)
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{
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ADataType v_a = a_m_k(m, k);
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BDataType v_b = b_k_n(k, n);
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v_acc +=
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ck_tile::type_convert<AccDataType>(v_a) * ck_tile::type_convert<AccDataType>(v_b);
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}
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CDataType v_c = 0;
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ck_tile::apply(
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[&](auto&&... t) {
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acc_element_op(v_c,
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ck_tile::type_convert<float>(v_acc),
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ck_tile::type_convert<float>(t(m, n))...);
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},
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ds_m_n_tuple);
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c_m_n(m, n) = ck_tile::type_convert<CDataType>(v_c);
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};
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make_ParallelTensorFunctor(f_mk_kn_mn, M, N)(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 DsDataType,
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typename AccDataType,
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typename CDataType,
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typename ACCElementOp,
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typename DDataType = remove_cvref_t<std::tuple_element_t<0, DsDataType>>>
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CK_TILE_HOST void
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reference_gemm_multiple_d(const HostTensor<ADataType>& a_m_k,
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const HostTensor<BDataType>& b_k_n,
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const std::array<HostTensor<DDataType>, DsDataType::size()>& ds_m_n,
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HostTensor<CDataType>& c_m_n,
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const ACCElementOp& acc_element_op = {})
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{
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const std::size_t M = a_m_k.get_length(0);
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const std::size_t N = b_k_n.get_length(1);
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const std::size_t K = a_m_k.get_length(1);
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auto f_mk_kn_mn = [&](auto m, auto n) {
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AccDataType v_acc = 0;
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for(std::size_t k = 0; k < K; ++k)
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{
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ADataType v_a = a_m_k(m, k);
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BDataType v_b = b_k_n(k, n);
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v_acc +=
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ck_tile::type_convert<AccDataType>(v_a) * ck_tile::type_convert<AccDataType>(v_b);
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}
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CDataType v_c = 0;
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if constexpr(DsDataType::size() == 0)
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{
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acc_element_op(v_c, ck_tile::type_convert<float>(v_acc));
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}
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else if constexpr(DsDataType::size() == 1)
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{
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acc_element_op(v_c,
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ck_tile::type_convert<float>(v_acc),
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ck_tile::type_convert<float>(ds_m_n[0](m, n)));
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}
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else if constexpr(DsDataType::size() == 2)
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{
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acc_element_op(v_c,
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ck_tile::type_convert<float>(v_acc),
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ck_tile::type_convert<float>(ds_m_n[0](m, n)),
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ck_tile::type_convert<float>(ds_m_n[1](m, n)));
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}
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c_m_n(m, n) = ck_tile::type_convert<CDataType>(v_c);
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};
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make_ParallelTensorFunctor(f_mk_kn_mn, M, N)(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 AccDataType,
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typename CDataType,
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typename LayoutA,
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typename LayoutB,
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typename LayoutC>
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__global__ void naive_gemm_kernel(ADataType* A,
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BDataType* B,
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CDataType* C,
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ck_tile::index_t M,
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ck_tile::index_t N,
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ck_tile::index_t K,
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ck_tile::index_t strideA,
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ck_tile::index_t strideB,
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ck_tile::index_t strideC)
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{
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int idx = blockIdx.x * blockDim.x + threadIdx.x;
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int row = idx / N; // Compute row index
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int col = idx % N; // Compute column index
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if(row < M && col < N)
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{
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AccDataType acc = 0.0;
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for(int k = 0; k < K; ++k)
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{
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constexpr index_t packed_size_a = ck_tile::numeric_traits<ADataType>::PackedSize;
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constexpr index_t packed_size_b = ck_tile::numeric_traits<BDataType>::PackedSize;
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// Adjust indexing based on matrix layout
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int a_index = (std::is_same_v<LayoutA, tensor_layout::gemm::RowMajor>)
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? row * strideA + k
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: k * strideA + row;
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int b_index = (std::is_same_v<LayoutB, tensor_layout::gemm::ColumnMajor>)
|
|
? col * strideB + k
|
|
: k * strideB + col;
|
|
|
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AccDataType v_a;
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AccDataType v_b;
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if constexpr(std::is_same_v<ADataType, pk_int4_t>)
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{
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const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(A[a_index / packed_size_a]);
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if(k % 2 == 1)
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v_a = fp32_val.hi;
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else
|
|
v_a = fp32_val.lo;
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|
}
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else
|
|
{
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|
v_a = ck_tile::type_convert<AccDataType>(A[a_index]);
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|
}
|
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if constexpr(std::is_same_v<BDataType, pk_int4_t>)
|
|
{
|
|
const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(B[b_index / packed_size_b]);
|
|
if(k % 2 == 1)
|
|
v_b = fp32_val.hi;
|
|
else
|
|
v_b = fp32_val.lo;
|
|
}
|
|
else
|
|
{
|
|
v_b = ck_tile::type_convert<AccDataType>(B[b_index]);
|
|
}
|
|
acc += v_a * v_b;
|
|
}
|
|
|
|
int c_index = (std::is_same_v<LayoutC, tensor_layout::gemm::RowMajor>)
|
|
? row * strideC + col
|
|
: col * strideC + row;
|
|
C[c_index] = ck_tile::type_convert<CDataType>(acc);
|
|
}
|
|
}
|
|
|
|
template <typename ADataType,
|
|
typename BDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename LayoutA,
|
|
typename LayoutB,
|
|
typename LayoutC>
|
|
void reference_gemm_gpu(ADataType* a_ptr,
|
|
BDataType* b_ptr,
|
|
CDataType* c_ptr,
|
|
index_t M,
|
|
index_t N,
|
|
index_t K,
|
|
index_t stride_a,
|
|
index_t stride_b,
|
|
index_t stride_c)
|
|
{
|
|
int totalElements = M * N;
|
|
int numThreadsPerBlock = 256; // Common choice for threads per block
|
|
int numBlocks = (totalElements + numThreadsPerBlock - 1) / numThreadsPerBlock;
|
|
|
|
naive_gemm_kernel<ADataType, BDataType, AccDataType, CDataType, LayoutA, LayoutB, LayoutC>
|
|
<<<numBlocks, numThreadsPerBlock>>>(
|
|
a_ptr, b_ptr, c_ptr, M, N, K, stride_a, stride_b, stride_c);
|
|
|
|
return;
|
|
}
|
|
|
|
template <typename ADataType,
|
|
typename BDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename LayoutA,
|
|
typename LayoutB,
|
|
typename LayoutC>
|
|
void reference_batched_gemm_gpu(ADataType* a_ptr,
|
|
BDataType* b_ptr,
|
|
CDataType* c_ptr,
|
|
index_t M,
|
|
index_t N,
|
|
index_t K,
|
|
index_t stride_a,
|
|
index_t stride_b,
|
|
index_t stride_c,
|
|
index_t batch_stride_A,
|
|
index_t batch_stride_B,
|
|
index_t batch_stride_C,
|
|
index_t batch_count)
|
|
{
|
|
int totalElements = M * N;
|
|
int numThreadsPerBlock = 256; // Common choice for threads per block
|
|
int numBlocks = (totalElements + numThreadsPerBlock - 1) / numThreadsPerBlock;
|
|
|
|
for(index_t batch_id = 0; batch_id < batch_count; ++batch_id)
|
|
{
|
|
ADataType* d_ATemp = a_ptr + batch_id * batch_stride_A;
|
|
BDataType* d_BTemp = b_ptr + batch_id * batch_stride_B;
|
|
CDataType* d_CTemp = c_ptr + batch_id * batch_stride_C;
|
|
naive_gemm_kernel<ADataType, BDataType, AccDataType, CDataType, LayoutA, LayoutB, LayoutC>
|
|
<<<numBlocks, numThreadsPerBlock>>>(
|
|
d_ATemp, d_BTemp, d_CTemp, M, N, K, stride_a, stride_b, stride_c);
|
|
}
|
|
|
|
return;
|
|
}
|
|
} // namespace ck_tile
|