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https://github.com/ROCm/composable_kernel.git
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f846457a87
* 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>
[ROCm/composable_kernel commit: abf4bdb9a9]
217 lines
8.0 KiB
C++
217 lines
8.0 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 <vector>
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#include "conv_fwd_util.hpp"
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#include "profile_conv_bwd_weight_impl.hpp"
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int test_self()
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{
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bool pass = true;
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std::vector<ck::utils::conv::ConvParams> params;
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params.push_back({2, 128, 256, 256, {1, 1}, {7, 7}, {2, 2}, {1, 1}, {0, 0}, {0, 0}});
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params.push_back({2, 128, 256, 256, {3, 3}, {14, 14}, {1, 1}, {1, 1}, {1, 1}, {1, 1}});
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params.push_back({2, 128, 256, 256, {1, 1}, {3, 3}, {1, 1}, {1, 1}, {0, 0}, {0, 0}});
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for(auto& param : params)
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{
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// f32
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pass &= ck::profiler::profile_conv_bwd_weight_impl<2,
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float,
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float,
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float,
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ck::tensor_layout::convolution::NHWC,
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ck::tensor_layout::convolution::KYXC,
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ck::tensor_layout::convolution::NHWK>(
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1, // do_verification,
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1, // init_method,
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0, // do_log,
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1, // nrepeat,
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param.N,
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param.K,
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param.C,
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param.input_spatial_lengths,
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param.filter_spatial_lengths,
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param.GetOutputSpatialLengths(),
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param.conv_filter_strides,
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param.conv_filter_dilations,
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param.input_left_pads,
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param.input_right_pads,
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2);
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// fp16
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pass &= ck::profiler::profile_conv_bwd_weight_impl<2,
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ck::half_t,
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ck::half_t,
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ck::half_t,
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ck::tensor_layout::convolution::NHWC,
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ck::tensor_layout::convolution::KYXC,
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ck::tensor_layout::convolution::NHWK>(
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1, // do_verification,
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1, // init_method,
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0, // do_log,
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1, // nrepeat,
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param.N,
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param.K,
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param.C,
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param.input_spatial_lengths,
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param.filter_spatial_lengths,
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param.GetOutputSpatialLengths(),
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param.conv_filter_strides,
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param.conv_filter_dilations,
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param.input_left_pads,
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param.input_right_pads,
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2);
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}
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return pass;
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}
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int main(int argc, char* argv[])
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{
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int data_type = 0;
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int init_method = 0;
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// Conv shape
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ck::index_t N = 128;
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ck::index_t K = 256;
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ck::index_t C = 192;
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ck::index_t Y = 3;
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ck::index_t X = 3;
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ck::index_t Hi = 71;
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ck::index_t Wi = 71;
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ck::index_t conv_stride_h = 2;
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ck::index_t conv_stride_w = 2;
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ck::index_t conv_dilation_h = 1;
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ck::index_t conv_dilation_w = 1;
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ck::index_t in_left_pad_h = 1;
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ck::index_t in_left_pad_w = 1;
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ck::index_t in_right_pad_h = 1;
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ck::index_t in_right_pad_w = 1;
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ck::index_t split_k = 1;
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bool pass = true;
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if(argc == 1)
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{
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pass = test_self();
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}
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else
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{
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if(argc == 3)
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{
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data_type = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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}
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else if(argc == 19)
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{
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data_type = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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N = std::stoi(argv[3]);
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K = std::stoi(argv[4]);
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C = std::stoi(argv[5]);
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Y = std::stoi(argv[6]);
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X = std::stoi(argv[7]);
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Hi = std::stoi(argv[8]);
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Wi = std::stoi(argv[9]);
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conv_stride_h = std::stoi(argv[10]);
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conv_stride_w = std::stoi(argv[11]);
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conv_dilation_h = std::stoi(argv[12]);
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conv_dilation_w = std::stoi(argv[13]);
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in_left_pad_h = std::stoi(argv[14]);
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in_left_pad_w = std::stoi(argv[15]);
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in_right_pad_h = std::stoi(argv[16]);
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in_right_pad_w = std::stoi(argv[17]);
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split_k = std::stoi(argv[18]);
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}
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else
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{
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printf("arg1: data type (0=fp32, 1=fp16, 2= bfp16, 3= int8_t )\n");
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printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
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printf("arg3 to 17: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, "
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"RightPx\n");
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exit(1);
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}
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ck::utils::conv::ConvParams param{2,
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N,
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K,
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C,
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{Y, X},
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{Hi, Wi},
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{conv_stride_h, conv_stride_w},
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{conv_dilation_h, conv_dilation_w},
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{in_left_pad_h, in_left_pad_w},
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{in_right_pad_h, in_right_pad_w}};
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if(data_type == 0)
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{
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pass = ck::profiler::profile_conv_bwd_weight_impl<2,
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float,
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float,
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float,
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ck::tensor_layout::convolution::NHWC,
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ck::tensor_layout::convolution::KYXC,
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ck::tensor_layout::convolution::NHWK>(
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1,
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init_method,
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0,
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1,
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param.N,
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param.K,
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param.C,
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param.input_spatial_lengths,
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param.filter_spatial_lengths,
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param.GetOutputSpatialLengths(),
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param.conv_filter_strides,
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param.conv_filter_dilations,
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param.input_left_pads,
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param.input_right_pads,
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split_k);
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}
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else if(data_type == 1)
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{
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pass = ck::profiler::profile_conv_bwd_weight_impl<2,
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ck::half_t,
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ck::half_t,
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ck::half_t,
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ck::tensor_layout::convolution::NHWC,
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ck::tensor_layout::convolution::KYXC,
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ck::tensor_layout::convolution::NHWK>(
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1,
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init_method,
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0,
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1,
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param.N,
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param.K,
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param.C,
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param.input_spatial_lengths,
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param.filter_spatial_lengths,
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param.GetOutputSpatialLengths(),
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param.conv_filter_strides,
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param.conv_filter_dilations,
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param.input_left_pads,
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param.input_right_pads,
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split_k);
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}
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else
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{
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std::cout << "Not support data type" << std::endl;
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return 1;
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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 conv2d bwd weight : 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 conv2d bwd weight: Fail " << std::endl;
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return -1;
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}
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}
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