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https://github.com/ROCm/composable_kernel.git
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ad57f6ef0b
* Wrap ck host utitlies in CK namespace. The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace. Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace. There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library. * Add using declarations to test code. After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace. * Add using declarations to client examples.
107 lines
3.0 KiB
C++
107 lines
3.0 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <algorithm>
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#include <cstddef>
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#include <iostream>
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#include <stdexcept>
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#include <string>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.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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#include "ck/library/reference_tensor_operation/cpu/reference_gemm.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/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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using ::ck::DeviceMem;
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using ::ck::HostTensorDescriptor;
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using ::ck::Tensor;
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using Row = ck::tensor_layout::gemm::RowMajor;
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using Col = ck::tensor_layout::gemm::ColumnMajor;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using AddMultiply = ck::tensor_operation::element_wise::AddMultiply;
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using BF16 = ck::bhalf_t;
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using F16 = ck::half_t;
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using F32 = float;
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using I8 = int8_t;
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using I32 = int32_t;
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struct ProblemSize final
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{
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ck::index_t M = 3840;
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ck::index_t N = 4096;
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ck::index_t K = 4096;
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ck::index_t StrideA = 4096;
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ck::index_t StrideB = 4096;
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ck::index_t StrideD0 = 0;
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ck::index_t StrideD1 = 4096;
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ck::index_t StrideE = 4096;
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};
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struct ExecutionConfig final
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{
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bool do_verification = true;
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int init_method = 1;
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bool time_kernel = false;
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};
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inline bool
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parse_cmd_args(int argc, char* argv[], ProblemSize& problem_size, ExecutionConfig& config)
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{
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if(argc == 1)
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{
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// use default case
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}
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else if(argc == 4)
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{
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config.do_verification = std::stoi(argv[1]);
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config.init_method = std::stoi(argv[2]);
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config.time_kernel = std::stoi(argv[3]);
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}
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else if(argc == 12)
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{
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config.do_verification = std::stoi(argv[1]);
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config.init_method = std::stoi(argv[2]);
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config.time_kernel = std::stoi(argv[3]);
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problem_size.M = std::stoi(argv[4]);
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problem_size.N = std::stoi(argv[5]);
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problem_size.K = std::stoi(argv[6]);
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problem_size.StrideA = std::stoi(argv[7]);
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problem_size.StrideB = std::stoi(argv[8]);
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problem_size.StrideD0 = std::stoi(argv[9]);
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problem_size.StrideD1 = std::stoi(argv[10]);
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problem_size.StrideE = std::stoi(argv[11]);
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}
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else
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{
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std::cerr << "arg1: verification (0=no, 1=yes)" << std::endl
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<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)"
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<< std::endl
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<< "arg3: time kernel (0=no, 1=yes)" << std::endl
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<< "arg4 to 10: M (256x), N(128x), K(32x), StrideA, StrideB, StrideD0, StrideD1, "
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"StrideE"
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<< std::endl;
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return false;
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}
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return true;
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}
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