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