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https://github.com/ROCm/composable_kernel.git
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abf4bdb9a9
* 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>
155 lines
6.6 KiB
C++
155 lines
6.6 KiB
C++
#include <algorithm>
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#include <cstdlib>
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#include <half.hpp>
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#include <iostream>
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#include <numeric>
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#include <tuple>
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#include <vector>
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#include "gemm_util.hpp"
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#include "config.hpp"
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#include "print.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 "host_gemm.hpp"
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#include "device_tensor.hpp"
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#include "device_gemm_xdl.hpp"
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#include "device_gemm_xdl_c_shuffle.hpp"
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#include "element_wise_operation.hpp"
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#include "reference_gemm.hpp"
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#include "gemm_specialization.hpp"
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using DeviceGemmNoOpPtr =
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ck::tensor_operation::device::DeviceGemmPtr<ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough>;
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namespace ck {
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namespace tensor_operation {
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namespace device {
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namespace device_gemm_instance {
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void add_device_gemm_xdl_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_gemm_xdl_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_gemm_xdl_f32_f32_f32_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_gemm_xdl_splitk_f32_f32_f32_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_nk_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_kn_mn_instances(std::vector<DeviceGemmNoOpPtr>&);
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} // namespace device_gemm_instance
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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int main()
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{
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using ADataType = float;
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using BDataType = float;
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using CDataType = float;
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using RowMajor = ck::tensor_layout::gemm::RowMajor;
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using ColumnMajor = ck::tensor_layout::gemm::ColumnMajor;
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bool res = true;
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std::vector<DeviceGemmNoOpPtr> gemmPtrs;
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_f32_f32_f32_km_kn_mn_instances(gemmPtrs);
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_splitk_f32_f32_f32_km_kn_mn_instances(gemmPtrs);
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_kn_mn_instances(gemmPtrs);
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for(auto& gemmPtr : gemmPtrs)
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{
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res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
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ADataType,
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BDataType,
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CDataType,
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ColumnMajor,
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RowMajor,
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RowMajor,
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PassThrough,
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PassThrough,
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PassThrough>{}(gemmPtr);
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}
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gemmPtrs.clear();
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_f32_f32_f32_km_nk_mn_instances(gemmPtrs);
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances(gemmPtrs);
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_c_shuffle_f32_f32_f32_km_nk_mn_instances(gemmPtrs);
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for(auto& gemmPtr : gemmPtrs)
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{
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res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
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ADataType,
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BDataType,
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CDataType,
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ColumnMajor,
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ColumnMajor,
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RowMajor,
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PassThrough,
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PassThrough,
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PassThrough>{}(gemmPtr);
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}
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gemmPtrs.clear();
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_f32_f32_f32_mk_kn_mn_instances(gemmPtrs);
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_splitk_f32_f32_f32_mk_kn_mn_instances(gemmPtrs);
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_kn_mn_instances(gemmPtrs);
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for(auto& gemmPtr : gemmPtrs)
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{
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res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
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ADataType,
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BDataType,
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CDataType,
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RowMajor,
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RowMajor,
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RowMajor,
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PassThrough,
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PassThrough,
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PassThrough>{}(gemmPtr);
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}
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gemmPtrs.clear();
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_f32_f32_f32_mk_nk_mn_instances(gemmPtrs);
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_splitk_f32_f32_f32_mk_nk_mn_instances(gemmPtrs);
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_c_shuffle_f32_f32_f32_mk_nk_mn_instances(gemmPtrs);
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for(auto& gemmPtr : gemmPtrs)
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{
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res &= ck::gemm_util::TestGemm<DeviceGemmNoOpPtr,
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ADataType,
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BDataType,
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CDataType,
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RowMajor,
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ColumnMajor,
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RowMajor,
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PassThrough,
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PassThrough,
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PassThrough>{}(gemmPtr);
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}
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std::cout << "TestGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
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return res ? 0 : 1;
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}
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