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[CK-Tile] Universal gemm memory bound pipeline (#1558)

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* CK-Tile GEMM with memory bound pipeline.

* Memory bound gemm pipeline.

* Fix not closed namespace.

* Block gemm mem pipeline draft.

* Do not use ck_tile:: within ck_tile namespace.

* Refactoring & Move Layout info to pipeline problem.

* Get hot loop and TailNum information before lunching kernel.

* Fixes in pipeline.

* Add comment to load_tile_raw and change variable naming style.

* Few small changes & formatting.

* Do not use macro.

* Add gtests.

* Use AccDataType for Output of MFMA instruction.

* Formatting.

* Refactor gemm examples.

* Switch over to current block gemm.

* Use currently available pipeline policy.

* Refactoring and review comment.s

* Fixes after merge.

* Add missing include.

* Add load tile overload which accepts output tensor as parameter.

* This give 8% perf boost at the cost of using more registers.

* Rename example.

* Small changes.

* Fix compilation err and lower K.

* Support different layouts for A/B

* Fix vector size for different layouts.

* Rename Alignment into VectorSize

* Unblock tests.
This commit is contained in:
Adam Osewski
2024-10-30 10:05:15 +01:00
committed by GitHub
parent 3d60953477
commit 24d996aae1
29 changed files with 1660 additions and 561 deletions
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56
include/ck_tile/host/reference/reference_gemm.hpp
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@@ -1,12 +1,13 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include <thread>
#include "ck_tile/core.hpp"
#include "ck_tile/host/host_tensor.hpp"
#include "ck_tile/ops/common/tensor_layout.hpp"
#include <thread>
namespace ck_tile {
@@ -14,55 +15,36 @@ template <typename ADataType,
typename BDataType,
typename AccDataType,
typename CDataType,
typename LayoutA,
typename LayoutB,
typename LayoutC,
typename AElementOp = ck_tile::identity,
typename BElementOp = ck_tile::identity,
typename ACCElementOp = ck_tile::identity>
CK_TILE_HOST void reference_gemm(const HostTensor<ADataType>& a_m_k,
const HostTensor<BDataType>& b_n_k,
const HostTensor<BDataType>& b_k_n,
HostTensor<CDataType>& c_m_n,
const AElementOp& a_element_op = {},
const BElementOp& b_element_op = {},
const ACCElementOp& acc_element_op = {})
{
const int N = (std::is_same_v<LayoutB, tensor_layout::gemm::ColumnMajor>)
? b_n_k.mDesc.get_lengths()[0]
: b_n_k.mDesc.get_lengths()[1];
const int K = (std::is_same_v<LayoutA, tensor_layout::gemm::RowMajor>)
? a_m_k.mDesc.get_lengths()[1]
: a_m_k.mDesc.get_lengths()[0];
const int M = (std::is_same_v<LayoutA, tensor_layout::gemm::RowMajor>)
? a_m_k.mDesc.get_lengths()[0]
: a_m_k.mDesc.get_lengths()[1];
const std::size_t M = a_m_k.get_length(0);
const std::size_t N = b_k_n.get_length(1);
const std::size_t K = a_m_k.get_length(1);
auto f = [&](auto m) {
for(int n = 0; n < N; ++n)
auto f_mn = [&](auto m, auto n) {
AccDataType v_acc = 0;
for(std::size_t k = 0; k < K; ++k)
{
AccDataType v_acc = 0;
ADataType v_a = a_element_op(a_m_k(m, k));
BDataType v_b = b_element_op(b_k_n(k, n));
for(int k = 0; k < K; ++k)
{
ADataType v_a = (std::is_same_v<LayoutA, tensor_layout::gemm::RowMajor>)
? a_element_op(a_m_k(m, k))
: a_element_op(a_m_k(k, m));
BDataType v_b = (std::is_same_v<LayoutB, tensor_layout::gemm::ColumnMajor>)
? b_element_op(b_n_k(n, k))
: b_element_op(b_n_k(k, n));
v_acc += ck_tile::type_convert<AccDataType>(v_a) *
ck_tile::type_convert<AccDataType>(v_b);
}
CDataType& c_ref = (std::is_same_v<LayoutC, tensor_layout::gemm::RowMajor>)
? c_m_n(m, n)
: c_m_n(n, m);
c_ref = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
v_acc +=
ck_tile::type_convert<AccDataType>(v_a) * ck_tile::type_convert<AccDataType>(v_b);
}
c_m_n(m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
};
make_ParallelTensorFunctor(f, M)(std::thread::hardware_concurrency());
make_ParallelTensorFunctor(f_mn, M, N)(std::thread::hardware_concurrency());
}
template <typename ADataType,