Files
composable_kernel/test/magic_number_division/magic_number_division.cpp
Adam Osewski abf4bdb9a9 Common forward convolution utility refactor. (#141)
* Convolution ND

* Code unification across dimensions for generating tensor descriptors.
* Example
* Instances

* Move convnd f32 instance file to comply with repo structure.

* Conv 1D tensor layouts.

* Formatting and use ReferenceConv

* Reference ConvFwd supporting 1D and 2D convolution.

* Debug printing TensorLayout name.

* Conv fwd 1D instance f32

* Refactor conv ND example.

Needed to support various conv dimensio.

Needed to support various conv dimensions

* Rename conv nd example director to prevent conflicts.

* Refactor some common utility to single file.

Plus some tests.

* Refactor GetHostTensorDescriptor + UT.

* Add 1D test case.

* Test reference convolution 1d/2d

* Remove some leftovers.

* Fix convolution example error for 1D

* Refactor test check errors utility function.

* Test Conv2D Fwd XDL

* More UT for 1D case.

* Parameterize input & weight initializers.

* Rename example to prevent conflicts.

* Split convnd instance into separate files for 1d/2d

* Address review comments.

* Fix data type for flops/gbytes calculations.

* Assign example number 11.

* 3D cases for convolution utility functions.

* 3D reference convolution.

* Add support for 3D convolution.

* Check for inputs bigger than  2GB.

* Formatting

* Support for bf16/f16/f32/i8 - conv instances + UT.

* Use check_err from test_util.hpp.

* Split convnd test into separate files for each dim.

* Fix data generation and use proper instances.

* Formatting

* Skip tensor initialization if not necessary.

* Fix CMakefiles.

* Remove redundant conv2d_fwd test.

* Lower problem size for conv3D UT.

* 3D case for convnd example.

* Remove leftovers after merge.

* Add Conv Specialization string to GetTypeString

* Skip instance causing numerical errors.

* Small fixes.

* Remove redundant includes.

* Fix namespace name error.

* Script for automatic testing and logging convolution fwd UTs

* Comment out numactl cmd.

* Refine weights initalization and relax rtol for fp16

* Move test_util.hpp to check_err.hpp

* Refine weights initalization and relax rtol for fp16

* Refactor common part of test conv utils.

* Move utility function to single common place.

* Add additional common functions to utility.

* Refactor convnd_fwd_xdl examples.

* Remove redundant files.
* Unify structure.

* Add constructor to ConvParams.

* And add input parameters validation.

* Modify conv examples to use single utility file.

* Remove check_error from host_tensor.hpp

* Get rid of check_indices function.

* Remove bf16_to_f32 function overload for scalars.

* Fix namespace.

* Add half_float::half for check_err.

* Fix conv params size in UT.

* Fix weights initialization for int8.

* Fix weights initialization for int8.

* Add type_convert when store output in ref conv 1D.

* Get back old conv2d_fwd_xdl operation.

* Silence conv debug print.

* format

* clean

* clean

* Fix merge.

* Fix namespace for check_err

* Formatting.

* Fix merge artifacts.

* Remove deleted header.

* Fix some includes and use ck::utils::check_err.

* Remove unused check_indices restored by previous merge.

* Fix namespaces after merge.

* Fix compilation error.

* Small fixes.

* Use common functions.
* Fix filename
* Fix namespaces.

* Fix merge artifact - retrieve removed by accident fun.

* Fix ConvForwardSpecialization.

* Adhere to coding style rules.

* Fix merge artifacts.

Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
2022-04-05 15:16:59 -05:00

151 lines
4.5 KiB
C++

#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>
#include <half.hpp>
#include "check_err.hpp"
#include "config.hpp"
#include "magic_division.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
__global__ void gpu_magic_number_division(uint32_t magic_multiplier,
uint32_t magic_shift,
const int32_t* p_dividend,
int32_t* p_result,
uint64_t num)
{
uint64_t global_thread_num = blockDim.x * gridDim.x;
uint64_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x;
for(uint64_t data_id = global_thread_id; data_id < num; data_id += global_thread_num)
{
p_result[data_id] =
ck::MagicDivision::DoMagicDivision(p_dividend[data_id], magic_multiplier, magic_shift);
}
}
__global__ void
gpu_naive_division(int32_t divisor, const int32_t* p_dividend, int32_t* p_result, uint64_t num)
{
uint64_t global_thread_num = blockDim.x * gridDim.x;
uint64_t global_thread_id = blockIdx.x * blockDim.x + threadIdx.x;
for(uint64_t data_id = global_thread_id; data_id < num; data_id += global_thread_num)
{
p_result[data_id] = p_dividend[data_id] / divisor;
}
}
__host__ void cpu_magic_number_division(uint32_t magic_multiplier,
uint32_t magic_shift,
const int32_t* p_dividend,
int32_t* p_result,
uint64_t num)
{
for(uint64_t data_id = 0; data_id < num; ++data_id)
{
p_result[data_id] =
ck::MagicDivision::DoMagicDivision(p_dividend[data_id], magic_multiplier, magic_shift);
}
}
int main(int, char*[])
{
uint64_t num_divisor = 4096;
uint64_t num_dividend = 1L << 16;
std::vector<int32_t> divisors_host(num_divisor);
std::vector<int32_t> dividends_host(num_dividend);
// generate divisor
for(uint64_t i = 0; i < num_divisor; ++i)
{
divisors_host[i] = i + 1;
}
// generate dividend
for(uint64_t i = 0; i < num_divisor; ++i)
{
dividends_host[i] = i;
}
DeviceMem dividends_dev_buf(sizeof(int32_t) * num_dividend);
DeviceMem naive_result_dev_buf(sizeof(int32_t) * num_dividend);
DeviceMem magic_result_dev_buf(sizeof(int32_t) * num_dividend);
std::vector<int32_t> naive_result_host(num_dividend);
std::vector<int32_t> magic_result_host(num_dividend);
std::vector<int32_t> magic_result_host2(num_dividend);
dividends_dev_buf.ToDevice(dividends_host.data());
bool pass = true;
for(std::size_t i = 0; i < num_divisor; ++i)
{
// run naive division on GPU
gpu_naive_division<<<1024, 256>>>(
divisors_host[i],
static_cast<const int32_t*>(dividends_dev_buf.GetDeviceBuffer()),
static_cast<int32_t*>(naive_result_dev_buf.GetDeviceBuffer()),
num_dividend);
// calculate magic number
uint32_t magic_multiplier, magic_shift;
ck::tie(magic_multiplier, magic_shift) =
ck::MagicDivision::CalculateMagicNumbers(divisors_host[i]);
// run magic division on GPU
gpu_magic_number_division<<<1024, 256>>>(
magic_multiplier,
magic_shift,
static_cast<const int32_t*>(dividends_dev_buf.GetDeviceBuffer()),
static_cast<int32_t*>(magic_result_dev_buf.GetDeviceBuffer()),
num_dividend);
naive_result_dev_buf.FromDevice(naive_result_host.data());
magic_result_dev_buf.FromDevice(magic_result_host.data());
bool res = ck::utils::check_err(magic_result_host, naive_result_host);
if(!res)
{
pass = false;
continue;
}
cpu_magic_number_division(magic_multiplier,
magic_shift,
dividends_host.data(),
magic_result_host2.data(),
num_dividend);
res = ck::utils::check_err(magic_result_host2, naive_result_host);
if(!res)
{
pass = false;
continue;
}
}
if(pass)
{
std::cout << "test magic number division: Pass" << std::endl;
return 0;
}
else
{
std::cout << "test magic number division: Fail" << std::endl;
return -1;
}
}