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30727c48fc
* [CK TILE STREAMK] Introduce initial support for tile engine in streamk GEMM. - This commit lays the groundwork for integrating the tile engine into streamk GEMM. It focuses on creating benchmark executables for streamk GEMM. - Additional scripts like test_benchmark.sh and gemm_benchmark.py will be added once the streamk implementation reaches stability. * [CK TILE STREAMK] Enable CI to execute tile engine benchmarks for StreamK GEMM * [CK TILE STREAMK] Refactor: Extract common utility functions. * [CK TILE STREAMK] Revise tile engine of streamk to align with the updated implementation * Add pre-commit * [CK TILE STREAMK] Add 'dp_persistent' and 'reduction_strategy' in output of CK TILE STREAMK * [CK TILE STREAMK] Fix a bug about value of 'dp_persistent' of CK TILE STREAMK * [CK TILE STREAMK] Update Jenkinsfile * [CK TILE Engine] Update StreamK tile engine help message Remove default value messages as they are automatically printed * [CK TILE Engine] Update StreamK tile engine - Remove namespace reboot * [CK TILE Engine] Update StreamK tile engine - Fix merge error
51 lines
2.4 KiB
C++
51 lines
2.4 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c), Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
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auto calculate_rtol_atol(const ck_tile::index_t K,
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const ck_tile::index_t kbatch,
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const float max_accumulated_value)
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{
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using ComputeType =
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std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
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// Calculate thresholds
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const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
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ck_tile::integer_divide_ceil(K, kbatch));
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const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
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max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
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// Calculate error due to split_k accumulation
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const auto rtol_split_k =
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ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
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const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
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max_accumulated_value, kbatch);
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// Use higher threshold
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return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
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}
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/// @brief Function to compare the results of the device and host computations
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bool compare(std::string instanceName,
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ck_tile::index_t K,
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ck_tile::index_t kbatch,
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ck_tile::HostTensor<CDataType>& c_m_n_dev_result,
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ck_tile::HostTensor<CDataType>& c_m_n_host_result)
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{
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const float max_accumulated_value =
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*std::max_element(c_m_n_host_result.mData.begin(), c_m_n_host_result.mData.end());
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const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
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K, kbatch, max_accumulated_value);
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bool pass = ck_tile::check_err(c_m_n_dev_result,
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c_m_n_host_result,
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"Error: Incorrect results!",
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rtol_atol.at(ck_tile::number<0>{}),
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rtol_atol.at(ck_tile::number<1>{}));
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std::cout << "For " << instanceName << " Relative error threshold is "
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<< rtol_atol.at(ck_tile::number<0>{}) << " Absolute error threshold is "
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<< rtol_atol.at(ck_tile::number<1>{}) << std::endl;
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std::cout << "The verification result is:" << (pass ? "correct" : "fail") << std::endl;
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return pass;
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}
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