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https://github.com/ROCm/composable_kernel.git
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Add examples for reduction fp16/fp32/bp16/int8/fp64 for 3d/4d/5d (#342)
* Update the reduce_blockwise example to support user specified data type and input+reducing dimensions
* Add examples for using reduce_multiblock_atomic_add
* Add more running examples to the default command-line
* Remove un-necessary header including
* Update to the example README.md
[ROCm/composable_kernel commit: 14932e8de3]
This commit is contained in:
Qianfeng
212
example/12_reduce/reduce_multiblock_atomic_add.cpp
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212
example/12_reduce/reduce_multiblock_atomic_add.cpp
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <initializer_list>
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#include <cstdlib>
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#include <getopt.h>
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#include "ck/utility/reduction_enums.hpp"
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#include "reduce_multiblock_atomic_add_impl.hpp"
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#include "reduce_example_common.hpp"
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using namespace ck;
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using namespace ck::tensor_operation::device;
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static struct option long_options[] = {{"inLengths", required_argument, nullptr, 'D'},
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{"verify", required_argument, nullptr, 'v'},
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{"help", no_argument, nullptr, '?'},
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{nullptr, 0, nullptr, 0}};
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class SimpleAppArgs
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{
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private:
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int option_index = 0;
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public:
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std::vector<size_t> inLengths = {16, 64, 32, 960};
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std::vector<int> reduceDims = {0, 1, 2};
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std::vector<float> scales = {1.0f, 0.0f};
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bool do_verification = true;
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int data_type = 1;
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int init_method = 2;
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bool time_kernel = true;
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public:
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void show_usage(const char* cmd)
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{
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std::cout << "Usage of " << cmd << std::endl;
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std::cout << "--inLengths or -D, comma separated list of input tensor dimension lengths"
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<< std::endl;
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std::cout << "--reduceDims or -R, comma separated list of to-reduce dimensions"
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<< std::endl;
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std::cout << "--verify or -v, 1/0 to indicate whether to verify the reduction result by "
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"comparing with the host-based reduction"
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<< std::endl;
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std::cout << "Arg1: data type (0: fp32, 1: fp64)" << std::endl;
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std::cout << "Arg2 -- init method (0=no init, 1=single integer value, 2=scope integer "
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"value, 3=decimal value)"
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<< std::endl;
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std::cout << "Arg3 -- time kernel (0=no, 1=yes)" << std::endl;
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};
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int processArgs(int argc, char* argv[])
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{
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using ck::host_common::getTypeValuesFromString;
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int ch;
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while(1)
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{
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ch = getopt_long(argc, argv, "D:R:v:l:", long_options, &option_index);
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if(ch == -1)
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break;
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switch(ch)
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{
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case 'D':
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if(!optarg)
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throw std::runtime_error("Invalid option format!");
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inLengths = getTypeValuesFromString<size_t>(optarg);
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break;
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case 'R':
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if(!optarg)
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throw std::runtime_error("Invalid option format!");
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reduceDims = getTypeValuesFromString<int>(optarg);
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break;
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case 'v':
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if(!optarg)
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throw std::runtime_error("Invalid option format!");
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do_verification = static_cast<bool>(std::atoi(optarg));
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break;
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case '?':
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if(std::string(long_options[option_index].name) == "help")
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{
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show_usage(argv[0]);
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return (-1);
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};
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break;
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default: show_usage(argv[0]); return (-1);
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};
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};
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if(optind + 3 > argc)
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{
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throw std::runtime_error("Invalid cmd-line arguments, more argumetns are needed!");
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};
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data_type = std::atoi(argv[optind++]);
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init_method = std::atoi(argv[optind++]);
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time_kernel = static_cast<bool>(std::atoi(argv[optind]));
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if(scales.empty())
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{
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scales.push_back(1.0f);
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scales.push_back(0.0f);
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};
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return (0);
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};
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};
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template <typename InOutDataType,
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typename AccDataType,
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ReduceTensorOp ReduceOpId,
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index_t PropagateNan>
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bool reduce_multiblock_atomic_add_test(bool do_verification,
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int init_method,
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bool time_kernel,
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const std::vector<size_t>& inLengths,
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const std::vector<int>& reduceDims,
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float alpha,
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float beta)
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{
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bool matched = false;
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int result = 0;
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const auto tuple_object = reduce_shape_instances{};
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static_for<0, std::tuple_size<reduce_shape_instances>::value, 1>{}([&](auto i) {
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if(matched)
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return;
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using ShapeType = remove_cvref_t<decltype(std::get<i>(tuple_object))>;
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if(ShapeType::Rank_ != inLengths.size() || ShapeType::NumReduceDim_ != reduceDims.size())
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return;
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result = reduce_multiblock_atomic_add_impl<InOutDataType,
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AccDataType,
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ReduceOpId,
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ShapeType::Rank_,
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ShapeType::NumReduceDim_,
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PropagateNan>(
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do_verification, init_method, time_kernel, inLengths, reduceDims, alpha, beta);
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matched = true;
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});
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return (result == 0) ? true : false;
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};
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constexpr ReduceTensorOp ReduceOpId = ReduceTensorOp::AVG;
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constexpr bool PropagateNan = true;
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int main(int argc, char* argv[])
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{
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bool pass = true;
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if(argc > 1)
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{
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SimpleAppArgs arg;
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if(arg.processArgs(argc, argv) < 0)
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return (-1);
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if(arg.data_type == 0)
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{
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pass = reduce_multiblock_atomic_add_test<float, float, ReduceOpId, PropagateNan>(
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arg.do_verification,
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arg.init_method,
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arg.time_kernel,
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arg.inLengths,
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arg.reduceDims,
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arg.scales[0],
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arg.scales[1]);
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}
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else if(arg.data_type == 1)
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{
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pass = reduce_multiblock_atomic_add_test<double, double, ReduceOpId, PropagateNan>(
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arg.do_verification,
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arg.init_method,
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arg.time_kernel,
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arg.inLengths,
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arg.reduceDims,
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arg.scales[0],
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arg.scales[1]);
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}
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}
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else
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{
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// for testing float
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pass = pass && reduce_multiblock_atomic_add_test<float, float, ReduceOpId, PropagateNan>(
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true, 2, false, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
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// for testing double
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pass = pass && reduce_multiblock_atomic_add_test<double, double, ReduceOpId, PropagateNan>(
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true, 2, false, {16, 64, 32, 960}, {0, 1, 2}, 1.0f, 0.0f);
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// for testing 3D input
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pass = pass && reduce_multiblock_atomic_add_test<float, float, ReduceOpId, PropagateNan>(
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true, 2, false, {16, 64, 960}, {0, 1}, 1.0f, 0.0f);
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// for testing 5D input
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pass = pass && reduce_multiblock_atomic_add_test<float, float, ReduceOpId, PropagateNan>(
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true, 2, false, {16, 64, 32, 2, 960}, {0, 1, 2, 3}, 1.0f, 0.0f);
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};
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return (pass ? 0 : 1);
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};
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