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https://github.com/ROCm/composable_kernel.git
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674f7cdc0e
[CK] Remove Stream-K from old CK ## Motivation Since Stream-K has a CK Tile implementation, we no longer need Stream-K in old CK. Hence, this PR removes Stream-K from old CK. ## Technical Details All Stream-K artifacts in old CK have been removed including examples, tests, kernels, and CK profiler artifacts. ## Test Plan Ran a CI run on the branch before publishing PR. ## Test Result All tests passed. ## Submission Checklist - [x] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests. Co-authored-by: Claude Sonnet 4 <noreply@anthropic.com>
285 lines
8.2 KiB
C++
285 lines
8.2 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 <cstdlib>
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#include <iostream>
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#include <initializer_list>
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#include <numeric>
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#include <unordered_map>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.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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#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.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/library/reference_tensor_operation/cpu/reference_gemm.hpp"
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#include "ck/library/reference_tensor_operation/gpu/reference_gemm.hpp"
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#include "ck/host_utility/kernel_launch.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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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 = -1;
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ck::index_t StrideB = -1;
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ck::index_t StrideC = -1;
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};
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struct ProblemSizeSplitK 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 = -1;
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ck::index_t StrideB = -1;
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ck::index_t StrideC = -1;
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ck::index_t KBatch = 1;
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};
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struct ExecutionConfig final
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{
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// 0 - no verification, 1 - CPU, 2 - GPU, 3 - CPU + GPU
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int do_verification = 1;
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int init_method = 2;
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bool time_kernel = false;
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};
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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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template <typename ProblemType>
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bool parse_cmd_args(int, char*[], ProblemType&, ExecutionConfig&)
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{
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return false;
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}
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template <>
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bool parse_cmd_args<ProblemSize>(int argc,
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char* argv[],
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ProblemSize& problem_size,
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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 == 10)
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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.StrideC = std::stoi(argv[9]);
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}
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else
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{
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std::cerr
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<< "arg1: verification (0=no, 1=CPU, 2=GPU, 3=CPU and GPU)" << std::endl
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<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)" << std::endl
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<< "arg3: time kernel (0=no, 1=yes)" << std::endl
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<< "arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC (default: -1 or 0)"
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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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template <>
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bool parse_cmd_args<ProblemSizeSplitK>(int argc,
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char* argv[],
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ProblemSizeSplitK& problem_size,
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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 >= 10)
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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.StrideC = std::stoi(argv[9]);
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if(argc >= 11)
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{
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problem_size.KBatch = std::stoi(argv[10]);
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}
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}
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else
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{
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std::cerr
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<< "arg1: verification (0=no, 1=CPU, 2=GPU, 3=CPU and GPU)" << std::endl
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<< "arg2: initialization (0=no init, 1=integer value, 2=decimal value)" << std::endl
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<< "arg3: time kernel (0=no, 1=yes)" << std::endl
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<< "arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC (default: -1 or 0)"
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<< std::endl
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<< "arg10: KBatch" << 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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template <typename DataType, typename ComputeDataType = DataType>
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inline __host__ __device__ constexpr double get_rtol()
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{
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if constexpr(std::is_same_v<DataType, float> && std::is_same_v<ComputeDataType, ck::tf32_t>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, float>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, double>)
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{
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return 1e-6;
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}
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else if constexpr(std::is_same_v<DataType, ck::half_t>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
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{
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return 5e-2;
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}
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else if constexpr(std::is_same_v<DataType, int32_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, int8_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, ck::f8_t>)
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{
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return 1e-1; // 240 and 224 are acceptable
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}
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else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
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{
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return 1.5e-1; // 57344 and 49152 are acceptable
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}
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else
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{
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return 1e-3;
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}
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}
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template <typename DataType, typename ComputeDataType = DataType>
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inline __host__ __device__ constexpr double get_atol()
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{
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if constexpr(std::is_same_v<DataType, float> && std::is_same_v<ComputeDataType, ck::tf32_t>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, float>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, double>)
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{
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return 1e-6;
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}
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else if constexpr(std::is_same_v<DataType, ck::half_t>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
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{
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return 5e-2;
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}
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else if constexpr(std::is_same_v<DataType, int32_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, int8_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, ck::f8_t>)
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{
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return 16.1; // 240 and 224 are acceptable
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}
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else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
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{
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return 8192.1; // 57344 and 49152 are acceptable
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}
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else
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{
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return 1e-3;
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}
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}
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float i4_to_f32_gfx9(uint8_t i4)
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{
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static std::unordered_map<uint8_t, float> u = {{0b1000, -0.5000f},
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{0b1001, -0.4375f},
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{0b1010, -0.3750f},
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{0b1011, -0.3125f},
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{0b1100, -0.2500f},
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{0b1101, -0.1875f},
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{0b1110, -0.1250f},
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{0b1111, -0.0625f},
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{0b0, +0.0000f},
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{0b1, +0.0625f},
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{0b10, +0.1250f},
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{0b11, +0.1875f},
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{0b100, +0.2500f},
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{0b101, +0.3125f},
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{0b110, +0.3750f},
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{0b111, +0.4375f}};
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return u[i4];
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}
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