mirror of
https://github.com/ROCm/composable_kernel.git
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4cbde83cb6
* chore(copyright): update copyright header for tile_engine directory
* chore(copyright): update copyright header for script directory
* chore(copyright): update copyright header for test_data directory
* chore(copyright): update copyright header for python directory
* chore(copyright): update copyright header for profiler directory
[ROCm/composable_kernel commit: 0aadb4b2c4]
545 lines
22 KiB
C++
545 lines
22 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#pragma once
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#include <iomanip>
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#include <iostream>
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#include <typeinfo>
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#include "ck/ck.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_mx_gemm.hpp"
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#include "ck/library/tensor_operation_instance/gpu/gemm_mx.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/fill.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/literals.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3_mx.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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#include "ck/utility/data_type.hpp"
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namespace ck {
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namespace profiler {
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#if 1
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template <bool KLast>
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void preShuffleScaleBuffer(ck::e8m0_bexp_t* src, ck::e8m0_bexp_t* dst, int MN, int K)
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{
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int MNXdlPack = 2;
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int KXdlPack = 2;
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int XdlMNThread = 16;
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int XdlKThread = 64 / XdlMNThread;
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int K0 = K / KXdlPack / XdlKThread; // KRepeat
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// The 4 16x128 building blocks will be packed into 1 32x256 for F4
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// The 8 16x16x128 mfma will be packed into 1 32x32x256 for F4
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// unfold the MN32xK(256/32) scale buffer
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// 4 16 2 2
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// To XdlKThread-> XdlMNThread -> KXdlPack -> MNXdlPack
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// Then, MNRepeat->KRepeat
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for(int n = 0; n < MN; ++n)
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{
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for(int k = 0; k < K; ++k)
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{
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int n0 = n / (XdlMNThread * MNXdlPack); // i MNRepeat
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int tempn = n % (XdlMNThread * MNXdlPack);
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int n1 = tempn % XdlMNThread; // i XdlMNThread
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int n2 = tempn / XdlMNThread; // i MNXdlPack
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int k0 = k / (XdlKThread * KXdlPack); // i KRepeat
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int tempk = k % (XdlKThread * KXdlPack);
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int k1 = tempk % XdlKThread; // i XdlKThread
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int k2 = tempk / XdlKThread; // i KXdlPack
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int outputIndex = n0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread * K0 +
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k0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread +
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k1 * MNXdlPack * KXdlPack * XdlMNThread + n1 * MNXdlPack * KXdlPack +
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k2 * MNXdlPack + n2;
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// src[n * K + k] = ck::type_convert<ck::e8m0_bexp_t>(static_cast<float>(powf(2.0f, n2 +
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// k2 * MNXdlPack)));
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if constexpr(KLast)
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dst[outputIndex] = src[n * K + k];
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else
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dst[outputIndex] = src[k * MN + n];
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}
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}
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}
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void preShuffleBuffer(const ck::f4x2_pk_t* src, ck::f4x2_pk_t* dst, int N, int K, int NXdl)
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{
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int KPack = 16;
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int NLane = NXdl;
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int KLane = 64 / NLane;
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int K_pk = K / 2;
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int K0 = K_pk / (KLane * KPack);
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// K -> K0 KLane KPack
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// N -> N0 NLane
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// N, K -> N0 K0 KLane NLane KPack
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int tempk;
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for(int n = 0; n < N; ++n)
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{
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for(int k = 0; k < K_pk; ++k)
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{
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int n0 = n / NLane;
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int n1 = n % NLane;
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int k0 = k / (KLane * KPack);
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tempk = k % (KLane * KPack);
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int k1 = tempk / KPack;
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int k2 = tempk % KPack;
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int outputIndex = n0 * KPack * NLane * KLane * K0 + k0 * KPack * NLane * KLane +
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k1 * KPack * NLane + n1 * KPack + k2;
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dst[outputIndex] = src[n * K_pk + k];
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}
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}
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}
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#endif
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template <typename ADataType,
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typename BDataType,
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typename CDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout,
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int ScaleBlockSize>
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bool profile_gemm_mx_impl(int do_verification,
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int init_method,
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bool do_log,
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bool time_kernel,
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int M,
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int N,
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int K,
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int StrideA,
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int StrideB,
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int StrideC,
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int KBatch,
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int n_warmup,
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int n_iter,
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uint64_t rotating = 0)
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{
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using tensor_operation::device::instance::Col;
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using tensor_operation::device::instance::E8M0;
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using tensor_operation::device::instance::E8M0PK;
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using tensor_operation::device::instance::MFMA;
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using tensor_operation::device::instance::Row;
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constexpr bool BPreShuffle = is_same_v<BLayout, MFMA>;
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using BRefLayout = conditional_t<BPreShuffle, Col, BLayout>;
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if(K % ScaleBlockSize != 0)
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{
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throw std::runtime_error("wrong! K must be multiple of ScaleBlockSize.");
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};
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using XDataType = E8M0;
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using XPackedDataType = E8M0PK;
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using AScaleLayout = Row;
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using BScaleLayout = Col;
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auto f_host_tensor_descriptor =
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[](ck::index_t row, ck::index_t col, ck::index_t stride, auto layout) {
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using namespace ck::literals;
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if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
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return HostTensorDescriptor({row, col}, {stride, 1});
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else
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return HostTensorDescriptor({row, col}, {1, stride});
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};
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auto f_get_default_stride =
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[](ck::index_t row, ck::index_t col, ck::index_t stride, auto layout) {
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if(stride == -1)
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{
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// give a chance if stride is -1, return a default packed stride
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if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
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return static_cast<ck::index_t>(col);
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else
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return static_cast<ck::index_t>(row);
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}
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else
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return static_cast<ck::index_t>(stride);
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};
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auto Scale_Padded_M = (M + 32 - 1) / 32 * 32;
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auto Scale_Stride_AM =
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f_get_default_stride(Scale_Padded_M, K / ScaleBlockSize, -1, AScaleLayout{});
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auto Scale_Stride_BN = f_get_default_stride(K / ScaleBlockSize, N, -1, BScaleLayout{});
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Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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auto b_k_n =
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std::make_shared<Tensor<BDataType>>(f_host_tensor_descriptor(K, N, StrideB, BRefLayout{}));
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auto b_input = b_k_n;
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if constexpr(BPreShuffle)
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b_input = std::make_shared<Tensor<BDataType>>(
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f_host_tensor_descriptor(K, N, StrideB, BRefLayout{})); // use layout only for size
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// scales for A and B
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Tensor<XDataType> a_m_k_scale(f_host_tensor_descriptor(
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Scale_Padded_M, K / ScaleBlockSize, Scale_Stride_AM, AScaleLayout{}));
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Tensor<XDataType> b_k_n_scale(
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f_host_tensor_descriptor(K / ScaleBlockSize, N, Scale_Stride_BN, BScaleLayout{}));
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// shuffled scales for A and B
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Tensor<XDataType> a_shuffled_scale(f_host_tensor_descriptor(
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Scale_Padded_M, K / ScaleBlockSize, Scale_Stride_AM, AScaleLayout{}));
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Tensor<XDataType> b_shuffled_scale(
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f_host_tensor_descriptor(K / ScaleBlockSize, N, Scale_Stride_BN, BScaleLayout{}));
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Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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std::size_t total_gemm_needed =
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a_m_k.GetElementSpaceSizeInBytes() + b_k_n->GetElementSpaceSizeInBytes() +
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a_m_k_scale.GetElementSpaceSizeInBytes() + b_k_n_scale.GetElementSpaceSizeInBytes() +
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a_shuffled_scale.GetElementSpaceSizeInBytes() +
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b_shuffled_scale.GetElementSpaceSizeInBytes();
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int rotating_count = std::max(
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1,
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std::min(n_iter,
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static_cast<int>(std::ceil(static_cast<double>(rotating) / total_gemm_needed))));
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std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
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std::cout << "a_m_k_scale: " << a_m_k_scale.mDesc << std::endl;
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std::cout << "b_k_n: " << b_k_n->mDesc << std::endl;
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std::cout << "b_k_n_scale: " << b_k_n_scale.mDesc << std::endl;
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std::cout << "c_m_n: " << c_m_n_device_result.mDesc << std::endl;
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std::cout << "rotating count: " << rotating_count << std::endl;
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auto a_data_element = [](float x) {
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if constexpr(ck::is_same_v<ADataType, ck::f4x2_pk_t>)
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return ck::type_convert<ADataType>(ck::float2_t(x));
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else if constexpr(ck::packed_size_v<ADataType> == 32)
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return ck::type_convert<ADataType>(ck::float32_t(x));
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else if constexpr(ck::packed_size_v<ADataType> == 16)
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return ck::type_convert<ADataType>(ck::float16_t(x));
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else
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return ck::type_convert<ADataType>(x);
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};
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auto b_data_element = [](float x) {
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if constexpr(ck::is_same_v<BDataType, ck::f4x2_pk_t>)
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return ck::type_convert<BDataType>(ck::float2_t(x));
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else if constexpr(ck::packed_size_v<BDataType> == 32)
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return ck::type_convert<BDataType>(ck::float32_t(x));
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else if constexpr(ck::packed_size_v<BDataType> == 16)
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return ck::type_convert<BDataType>(ck::float16_t(x));
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else
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return ck::type_convert<BDataType>(x);
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};
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using int_distr = std::uniform_int_distribution<int>;
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using float_distr = std::uniform_real_distribution<float>;
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switch(init_method)
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{
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case 0: // Initializations for development and debugging
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ck::utils::FillConstant<ADataType>{a_data_element(1.0f)}(a_m_k);
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ck::utils::FillConstant<XDataType>{ck::type_convert<XDataType>(2.0f)}(a_m_k_scale);
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ck::utils::FillConstant<BDataType>{b_data_element(0.5f)}(*b_k_n);
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ck::utils::FillConstant<XDataType>{ck::type_convert<XDataType>(1.0f)}(b_k_n_scale);
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if(do_log)
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{
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std::cout << "Init A = {1}" << std::endl;
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std::cout << "Init A scale = {2.0}" << std::endl;
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std::cout << "Init B = {0.5}" << std::endl;
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std::cout << "Init B scale = {1.0}" << std::endl;
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std::cout << "Expect C = {K}" << std::endl;
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}
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break;
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case 1:
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a_m_k.GenerateTensorDistr(
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int_distr{-4, 4}, ck::identity{}, std::minstd_rand(time(nullptr))); // Z[-4,4]
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b_k_n->GenerateTensorDistr(int_distr{-4, 4}); // Z[-4,4]
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a_m_k_scale.GenerateTensorDistr(int_distr{125, 128}); // scales: {0.25, 0.5, 1, 2}
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b_k_n_scale.GenerateTensorDistr(int_distr{125, 128}); // scales: {0.25, 0.5, 1, 2}
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break;
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default:
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a_m_k.GenerateTensorDistr(
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float_distr{-2.0, 2.0}, ck::identity{}, std::minstd_rand(time(nullptr)));
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a_m_k_scale.GenerateTensorDistr(float_distr{powf(2.0f, -125.0f), 1.0f});
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b_k_n->GenerateTensorDistr(float_distr{-2.0, 2.0});
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b_k_n_scale.GenerateTensorDistr(float_distr{powf(2.0f, -125.0f), 1.0f});
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break;
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}
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#if 1
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preShuffleScaleBuffer<ck::is_same_v<ALayout, Row>>(a_m_k_scale.mData.data(),
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a_shuffled_scale.mData.data(),
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Scale_Padded_M,
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K / ScaleBlockSize);
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preShuffleScaleBuffer<ck::is_same_v<BRefLayout, Col>>(
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b_k_n_scale.mData.data(), b_shuffled_scale.mData.data(), N, K / ScaleBlockSize);
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if constexpr(BPreShuffle)
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{
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int NPerXdl = 16; // Fixed 16
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preShuffleBuffer(b_k_n->mData.data(), b_input->mData.data(), N, K, NPerXdl);
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}
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#endif
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using AElementOp = ck::tensor_operation::element_wise::PassThrough;
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using BElementOp = ck::tensor_operation::element_wise::PassThrough;
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using CElementOp = ck::tensor_operation::element_wise::PassThrough;
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const auto a_element_op = AElementOp{};
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const auto b_element_op = BElementOp{};
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const auto c_element_op = CElementOp{};
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if(do_log > 0)
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std::cout << "Device memory allocation..." << std::endl;
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DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.GetElementSpaceSize());
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DeviceMem a_scale_device_buf(sizeof(XDataType) * a_m_k_scale.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b_k_n->GetElementSpaceSize());
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DeviceMem b_scale_device_buf(sizeof(XDataType) * b_k_n_scale.GetElementSpaceSize());
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DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.GetElementSpaceSize());
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if(do_log > 0)
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std::cout << "Upload data to device..." << std::endl;
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a_device_buf.ToDevice(a_m_k.mData.data());
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a_scale_device_buf.ToDevice(a_shuffled_scale.mData.data());
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b_device_buf.ToDevice(b_input->mData.data());
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b_scale_device_buf.ToDevice(b_shuffled_scale.mData.data());
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if(do_log > 0)
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std::cout << "Done." << std::endl;
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using DeviceOp = ck::tensor_operation::device::DeviceGemmMX<ALayout,
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BLayout,
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CLayout,
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ADataType,
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XPackedDataType,
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BDataType,
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XPackedDataType,
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CDataType,
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ScaleBlockSize,
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AElementOp,
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BElementOp,
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CElementOp>;
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std::cout << "finding op instances..." << std::endl;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
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// Run reference GEMM
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if(do_verification)
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{
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceMXGemm< //
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ADataType,
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BDataType,
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CDataType,
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float, // AccDataType
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XDataType,
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AElementOp,
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BElementOp,
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CElementOp,
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float, // ComputeTypeA
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float // ComputeTypeB
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>;
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auto ref_gemm = ReferenceGemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument = ref_gemm.MakeArgument(a_m_k,
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a_m_k_scale,
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*b_k_n,
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b_k_n_scale,
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c_m_n_host_result,
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a_element_op,
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b_element_op,
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c_element_op);
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ref_invoker.Run(ref_argument);
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}
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std::string best_op_name;
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std::optional<std::string> best_op_object_name;
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float best_ave_time = 0;
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float best_tflops = 0;
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float best_gb_per_sec = 0;
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float best_kbatch = 0;
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bool pass = true;
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// profile device GEMM instances
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for(auto& op_ptr : op_ptrs)
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{
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std::vector<int> kbatch_list = {1, 2, 4, 8, 16, 19, 32, 38}; // use these when KBatch <= 0
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if(KBatch > 0)
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{
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kbatch_list = {KBatch};
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}
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for(std::size_t i = 0; i < kbatch_list.size(); i++)
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{
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auto kbatch_curr = kbatch_list[i];
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auto argument_ptr = op_ptr->MakeArgumentPointer(
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static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
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static_cast<XPackedDataType*>(a_scale_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
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static_cast<XPackedDataType*>(b_scale_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
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M,
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N,
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K,
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StrideA,
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Scale_Stride_AM,
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StrideB,
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Scale_Stride_BN,
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StrideC,
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kbatch_curr,
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a_element_op,
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b_element_op,
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c_element_op);
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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// re-init C to zero before profiling next kernel
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c_device_buf.SetZero();
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invoker_ptr->Run(argument_ptr.get(),
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StreamConfig{nullptr, false, 0, n_warmup, n_iter});
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if(do_verification)
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{
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c_device_buf.FromDevice(c_m_n_device_result.mData.data());
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if(do_log)
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{
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if(init_method == 0)
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{
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auto expected = static_cast<float>(K);
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auto computed = type_convert<float>(c_m_n_device_result(0, 12));
|
|
|
|
pass = pass & (std::abs(expected - computed) <= 0.0f);
|
|
std::cout << "\nExpected vs Computed: " << expected << " vs "
|
|
<< computed << ((pass) ? " (PASSED!)" : " (FAILED!)")
|
|
<< std::endl
|
|
<< std::endl;
|
|
}
|
|
else
|
|
{
|
|
if constexpr(is_same_v<ADataType, ck::f8_t> ||
|
|
is_same_v<ADataType, ck::bf8_t>)
|
|
LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",")
|
|
<< "\n";
|
|
else
|
|
std::cout << "A: WIP PRINT PACKED TYPE\n";
|
|
LogRangeAsType<float>(std::cout << "a_scale : ", a_m_k_scale.mData, ",")
|
|
<< "\n";
|
|
if constexpr(is_same_v<BDataType, ck::f8_t> ||
|
|
is_same_v<BDataType, ck::bf8_t>)
|
|
LogRangeAsType<float>(std::cout << "b : ", b_k_n->mData, ",")
|
|
<< "\n";
|
|
else
|
|
std::cout << "B: WIP PRINT PACKED TYPE\n";
|
|
LogRangeAsType<float>(std::cout << "b_scale: ", b_k_n_scale.mData, ",")
|
|
<< "\n";
|
|
LogRangeAsType<float>(
|
|
std::cout << "c_host : ", c_m_n_host_result.mData, ",")
|
|
<< "\n";
|
|
LogRangeAsType<float>(
|
|
std::cout << "c_device: ", c_m_n_device_result.mData, ",")
|
|
<< std::endl;
|
|
}
|
|
}
|
|
|
|
pass = pass & ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
|
|
}
|
|
|
|
std::string op_name = op_ptr->GetTypeString();
|
|
std::optional<std::string> op_obj_name = op_ptr->GetObjectName();
|
|
|
|
float ave_time = invoker_ptr->Run(argument_ptr.get(),
|
|
StreamConfig{nullptr,
|
|
time_kernel,
|
|
0,
|
|
n_warmup,
|
|
n_iter,
|
|
rotating_count > 1,
|
|
rotating_count});
|
|
|
|
// Output size(M*N) * [dot product(2K) + product of scales(K/ScaleBlockSize) +
|
|
// scaling of partial sums(K/ScaleBlockSize)]
|
|
// FLOPS = 2 * M * N * K + 2 * M * N * K / ScaleBlockSize
|
|
std::size_t flop =
|
|
std::size_t(2) * M * N * K + std::size_t(2) * M * N * K / ScaleBlockSize;
|
|
|
|
// TODO: fp6?
|
|
std::size_t num_btype = sizeof(ADataType) * M * K / packed_size_v<ADataType> +
|
|
sizeof(BDataType) * K * N / packed_size_v<BDataType> +
|
|
sizeof(CDataType) * M * N +
|
|
sizeof(XDataType) * (M * K + K * N) / ScaleBlockSize;
|
|
|
|
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 << ", KBatch "
|
|
<< kbatch_curr << std::endl;
|
|
|
|
if(tflops > best_tflops && ave_time > 1e-10)
|
|
{
|
|
best_op_name = op_name;
|
|
best_op_object_name = op_obj_name;
|
|
best_tflops = tflops;
|
|
best_ave_time = ave_time;
|
|
best_gb_per_sec = gb_per_sec;
|
|
best_kbatch = kbatch_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";
|
|
}
|
|
std::cout << " ALayout = " << ALayout::name;
|
|
std::cout << " BLayout = " << BLayout::name;
|
|
std::cout << " CLayout = " << CLayout::name;
|
|
|
|
std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
|
|
<< " StrideB = " << StrideB << " StrideC = " << StrideC << " KBatch = " << best_kbatch
|
|
<< " : " << best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec
|
|
<< " GB/s, " << best_op_name << std::endl;
|
|
|
|
if(best_op_object_name)
|
|
std::cout << best_op_object_name.value() << std::endl;
|
|
|
|
return pass;
|
|
}
|
|
|
|
} // namespace profiler
|
|
} // namespace ck
|