mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-20 14:59:17 +00:00
Clean up conv example, Instances, profiler and test (#324)
* convnd_fwd fp16 example * update example * update example * update instance * updating refernce conv * update reference conv * update conv fwd profiler * update conv 1d and 3d instance * update include path * clean * update profiler for conv bwd data and weight * update conv bwd weight * clean * update conv example * update profiler for conv bwd weight * update ckprofiler for conv bwd data * fix reference conv bwd data bug; update conv bwd data test * update examples * fix initialization issue * update test for conv fwd * clean * clean * remove test case too sensitive to error threshhold * fix test * clean * fix build * adding conv multiple d * adding conv multiple D * add matrix padder * add gemm padding to convnd * adding group conv * update gemm multi-d * refactor * refactor * refactor * clean * clean * refactor * refactor * reorg * add ds * add bias * clean * add G * adding group * adding group * adding group * update Tensor * clean * update example * update DeviceGemmMultipleD_Xdl_CShuffle * update conv bwd-data and bwd-weight * upate contraction example * update gemm and batch gemm with e permute * fix example build * instance for grouped conv1d * update example * adding group conv instance * update gemm bilinear instance * update gemm+add+add+fastgelu instance * update profiler * update profiler * update test * update test and client example * clean * add grouped conv into profiler * update profiler * clean * add test grouped conv, update all conv test to gtest * update test
This commit is contained in:
Chao Liu
@@ -1,2 +1,2 @@
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add_gtest_executable(test_reference_conv_fwd reference_conv_fwd.cpp)
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target_link_libraries(test_reference_conv_fwd PRIVATE host_tensor conv_util)
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target_link_libraries(test_reference_conv_fwd PRIVATE utility)
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@@ -13,74 +13,64 @@
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/conv_util.hpp"
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#include "ck/library/utility/fill.hpp"
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#include "ck/library/host_tensor/host_tensor.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/convolution_parameter.hpp"
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#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
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namespace {
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using InElementOp = ck::tensor_operation::element_wise::PassThrough;
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using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
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using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
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template <ck::index_t NDim,
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template <ck::index_t NDimSpatial,
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typename InDataType = float,
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typename WeiDataType = float,
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typename OutDataType = float,
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typename InLayout = ck::tensor_layout::convolution::NHWC,
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typename WeiLayout = ck::tensor_layout::convolution::KYXC,
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typename OutLayout = ck::tensor_layout::convolution::NHWK,
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typename InLayout = ck::tensor_layout::convolution::GNHWC,
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typename WeiLayout = ck::tensor_layout::convolution::GKYXC,
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typename OutLayout = ck::tensor_layout::convolution::GNHWK,
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typename FillInputOp = ck::utils::FillMonotonicSeq<InDataType>,
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typename FillWeightsOp = ck::utils::FillConstant<WeiDataType>>
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Tensor<OutDataType>
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run_reference_convolution_forward(const ck::utils::conv::ConvParams& params,
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run_reference_convolution_forward(const ck::utils::conv::ConvParam& conv_param,
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const FillInputOp& fill_input_op = FillInputOp{},
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const FillWeightsOp& fill_weights_op = FillWeightsOp{0.5f})
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{
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std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N_),
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static_cast<std::size_t>(params.C_)};
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input_dims.insert(std::end(input_dims),
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std::begin(params.input_spatial_lengths_),
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std::end(params.input_spatial_lengths_));
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const auto in_g_n_c_wis_desc =
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ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param);
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std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K_),
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static_cast<std::size_t>(params.C_)};
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filter_dims.insert(std::end(filter_dims),
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std::begin(params.filter_spatial_lengths_),
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std::end(params.filter_spatial_lengths_));
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const auto wei_g_k_c_xs_desc =
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ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(conv_param);
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const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
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std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N_),
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static_cast<std::size_t>(params.K_)};
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output_dims.insert(std::end(output_dims),
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std::begin(output_spatial_lengths),
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std::end(output_spatial_lengths));
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const auto out_g_n_k_wos_desc =
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ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(conv_param);
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Tensor<InDataType> input(ck::utils::conv::get_host_tensor_descriptor(input_dims, InLayout{}));
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Tensor<WeiDataType> weights(
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ck::utils::conv::get_host_tensor_descriptor(filter_dims, WeiLayout{}));
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Tensor<OutDataType> host_output(
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ck::utils::conv::get_host_tensor_descriptor(output_dims, OutLayout{}));
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Tensor<InDataType> input(in_g_n_c_wis_desc);
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Tensor<WeiDataType> weights(wei_g_k_c_xs_desc);
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Tensor<OutDataType> host_output(out_g_n_k_wos_desc);
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fill_input_op(input.begin(), input.end());
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fill_weights_op(weights.begin(), weights.end());
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std::fill(host_output.begin(), host_output.end(), OutDataType(0.f));
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auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<InDataType,
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auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
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InDataType,
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WeiDataType,
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OutDataType,
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InElementOp,
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WeiElementOp,
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OutElementOp,
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NDim>();
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OutElementOp>();
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auto ref_invoker = ref_conv.MakeInvoker();
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auto ref_argument = ref_conv.MakeArgument(input,
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weights,
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host_output,
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params.conv_filter_strides_,
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params.conv_filter_dilations_,
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params.input_left_pads_,
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params.input_right_pads_,
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conv_param.conv_filter_strides_,
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conv_param.conv_filter_dilations_,
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conv_param.input_left_pads_,
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conv_param.input_right_pads_,
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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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@@ -91,21 +81,29 @@ run_reference_convolution_forward(const ck::utils::conv::ConvParams& params,
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} // anonymous namespace
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TEST(ReferenceConvolutionFWD, Conv2DNHWC)
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// Eeference convolution assume dimensions of tensor descriptors are in GNCDHW/GKCZYX/GNKDHW order,
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// regardless of physical tensor layouts in memory.
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// Some tests below assume dimensions of tensor descriptors can be in other order, and therefore
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// are disabled
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// TODO: add more tests, which comply with assumption about dimension order of reference convolution
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// and add tests for more physical layout
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#if 0
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TEST(ReferenceConvolutionFWD, Conv2DGNHWC)
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{
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ck::utils::conv::ConvParams params;
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params.N_ = 1;
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params.K_ = 1;
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params.C_ = 2;
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params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3};
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params.input_spatial_lengths_ = std::vector<ck::index_t>{6, 6};
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params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1};
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params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1};
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params.input_left_pads_ = std::vector<ck::index_t>{0, 0};
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params.input_right_pads_ = std::vector<ck::index_t>{0, 0};
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ck::utils::conv::ConvParam conv_param(2,
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1,
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1,
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1,
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2,
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std::vector<ck::index_t>{3, 3},
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std::vector<ck::index_t>{6, 6},
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std::vector<ck::index_t>{1, 1},
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std::vector<ck::index_t>{1, 1},
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std::vector<ck::index_t>{0, 0},
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std::vector<ck::index_t>{0, 0});
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auto out_tensor = run_reference_convolution_forward<2>(params);
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std::vector<std::size_t> ref_dims{1, 1, 4, 4};
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auto out_tensor = run_reference_convolution_forward<2>(conv_param);
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std::vector<std::size_t> ref_dims{1, 1, 4, 4, 1};
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std::vector<float> ref_data{130.5,
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148.5,
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166.5,
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@@ -127,21 +125,22 @@ TEST(ReferenceConvolutionFWD, Conv2DNHWC)
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EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
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}
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TEST(ReferenceConvolutionFWD, Conv2DNHWCStridesDilationsPadding)
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TEST(ReferenceConvolutionFWD, Conv2DGNHWCStridesDilationsPadding)
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{
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ck::utils::conv::ConvParams params;
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params.N_ = 1;
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params.K_ = 2;
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params.C_ = 2;
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params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3};
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params.input_spatial_lengths_ = std::vector<ck::index_t>{12, 12};
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params.conv_filter_strides_ = std::vector<ck::index_t>{2, 2};
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params.conv_filter_dilations_ = std::vector<ck::index_t>{2, 2};
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params.input_left_pads_ = std::vector<ck::index_t>{1, 1};
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params.input_right_pads_ = std::vector<ck::index_t>{1, 1};
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ck::utils::conv::ConvParam conv_param(2,
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1,
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1,
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2,
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2,
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std::vector<ck::index_t>{3, 3},
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std::vector<ck::index_t>{12, 12},
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std::vector<ck::index_t>{2, 2},
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std::vector<ck::index_t>{2, 2},
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std::vector<ck::index_t>{1, 1},
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std::vector<ck::index_t>{1, 1});
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auto out_tensor = run_reference_convolution_forward<2>(params);
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std::vector<std::size_t> ref_dims = std::vector<std::size_t>{1, 2, 5, 5};
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auto out_tensor = run_reference_convolution_forward<2>(conv_param);
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std::vector<std::size_t> ref_dims = std::vector<std::size_t>{1, 5, 5, 2};
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std::vector<float> ref_data{
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210., 210., 327., 327., 351., 351., 375., 375., 399., 399.,
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459., 459., 706.5, 706.5, 742.5, 742.5, 778.5, 778.5, 814.5, 814.5,
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@@ -153,88 +152,88 @@ TEST(ReferenceConvolutionFWD, Conv2DNHWCStridesDilationsPadding)
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EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
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}
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TEST(ReferenceConvolutionFWD, Conv1DNWC)
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TEST(ReferenceConvolutionFWD, Conv1DGNWC)
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{
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ck::utils::conv::ConvParams params;
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params.num_dim_spatial_ = 1;
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params.N_ = 1;
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params.K_ = 1;
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params.C_ = 2;
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params.filter_spatial_lengths_ = std::vector<ck::index_t>{3};
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params.input_spatial_lengths_ = std::vector<ck::index_t>{6};
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params.conv_filter_strides_ = std::vector<ck::index_t>{1};
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params.conv_filter_dilations_ = std::vector<ck::index_t>{1};
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params.input_left_pads_ = std::vector<ck::index_t>{0};
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params.input_right_pads_ = std::vector<ck::index_t>{0};
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ck::utils::conv::ConvParam conv_param(1,
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1,
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1,
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1,
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2,
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std::vector<ck::index_t>{3},
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std::vector<ck::index_t>{6},
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std::vector<ck::index_t>{1},
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std::vector<ck::index_t>{1},
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std::vector<ck::index_t>{0},
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std::vector<ck::index_t>{0});
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auto out_tensor =
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run_reference_convolution_forward<1,
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float,
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float,
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float,
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ck::tensor_layout::convolution::NWC,
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ck::tensor_layout::convolution::KXC,
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ck::tensor_layout::convolution::NWK>(params);
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std::vector<std::size_t> ref_dims{1, 1, 4};
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ck::tensor_layout::convolution::GNWC,
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ck::tensor_layout::convolution::GKXC,
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ck::tensor_layout::convolution::GNWK>(conv_param);
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std::vector<std::size_t> ref_dims{1, 1, 4, 1};
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std::vector<float> ref_data{7.5, 13.5, 19.5, 25.5};
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EXPECT_TRUE(ck::utils::check_err(
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out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
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EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
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}
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TEST(ReferenceConvolutionFWD, Conv1DNWCStridesDilationsPadding)
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TEST(ReferenceConvolutionFWD, Conv1DGNWCStridesDilationsPadding)
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{
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ck::utils::conv::ConvParams params;
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params.num_dim_spatial_ = 1;
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params.N_ = 1;
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params.K_ = 2;
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params.C_ = 2;
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params.filter_spatial_lengths_ = std::vector<ck::index_t>{3};
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params.input_spatial_lengths_ = std::vector<ck::index_t>{12};
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params.conv_filter_strides_ = std::vector<ck::index_t>{2};
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params.conv_filter_dilations_ = std::vector<ck::index_t>{2};
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params.input_left_pads_ = std::vector<ck::index_t>{1};
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params.input_right_pads_ = std::vector<ck::index_t>{1};
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ck::utils::conv::ConvParam conv_param(1,
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1,
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1,
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2,
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2,
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std::vector<ck::index_t>{3},
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std::vector<ck::index_t>{12},
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std::vector<ck::index_t>{2},
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std::vector<ck::index_t>{2},
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std::vector<ck::index_t>{1},
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std::vector<ck::index_t>{1});
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auto out_tensor =
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run_reference_convolution_forward<1,
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float,
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float,
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float,
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ck::tensor_layout::convolution::NWC,
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ck::tensor_layout::convolution::KXC,
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ck::tensor_layout::convolution::NWK>(params);
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std::vector<std::size_t> ref_dims{1, 2, 5};
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ck::tensor_layout::convolution::GNWC,
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ck::tensor_layout::convolution::GKXC,
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ck::tensor_layout::convolution::GNWK>(conv_param);
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std::vector<std::size_t> ref_dims{1, 1, 5, 2};
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std::vector<float> ref_data{9., 9., 19.5, 19.5, 31.5, 31.5, 43.5, 43.5, 55.5, 55.5};
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EXPECT_TRUE(ck::utils::check_err(
|
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out_tensor.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
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EXPECT_TRUE(ck::utils::check_err(out_tensor.mData, ref_data, "Error: incorrect results!"));
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}
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TEST(ReferenceConvolutionFWD, Conv1DNWCSameOutputSize)
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TEST(ReferenceConvolutionFWD, Conv1DGNWCSameOutputSize)
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{
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ck::utils::conv::ConvParams params;
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params.num_dim_spatial_ = 1;
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params.N_ = 2;
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params.K_ = 16;
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params.C_ = 4;
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params.filter_spatial_lengths_ = std::vector<ck::index_t>{3};
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params.input_spatial_lengths_ = std::vector<ck::index_t>{16};
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params.conv_filter_strides_ = std::vector<ck::index_t>{1};
|
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params.conv_filter_dilations_ = std::vector<ck::index_t>{1};
|
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params.input_left_pads_ = std::vector<ck::index_t>{1};
|
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params.input_right_pads_ = std::vector<ck::index_t>{1};
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ck::utils::conv::ConvParam conv_param(1,
|
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1,
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2,
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16,
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4,
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std::vector<ck::index_t>{3},
|
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std::vector<ck::index_t>{16},
|
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std::vector<ck::index_t>{1},
|
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std::vector<ck::index_t>{1},
|
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std::vector<ck::index_t>{1},
|
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std::vector<ck::index_t>{1});
|
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|
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auto out_tensor2 = run_reference_convolution_forward<1,
|
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float,
|
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float,
|
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float,
|
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ck::tensor_layout::convolution::NWC,
|
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ck::tensor_layout::convolution::KXC,
|
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ck::tensor_layout::convolution::NWK>(
|
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params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
|
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ck::tensor_layout::convolution::GNWC,
|
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ck::tensor_layout::convolution::GKXC,
|
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ck::tensor_layout::convolution::GNWK>(
|
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conv_param, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
|
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|
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std::vector<std::size_t> ref_dims{2, 16, 16};
|
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std::vector<std::size_t> ref_dims{1, 2, 16, 16};
|
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std::vector<float> ref_data{
|
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1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4,
|
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1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4, 1.4,
|
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@@ -304,30 +303,31 @@ TEST(ReferenceConvolutionFWD, Conv1DNWCSameOutputSize)
|
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out_tensor2.mDesc.GetLengths(), ref_dims, "Error: wrong output tensor dimensions!"));
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EXPECT_TRUE(ck::utils::check_err(out_tensor2.mData, ref_data, "Error: incorrect results!"));
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||||
}
|
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#endif
|
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|
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TEST(ReferenceConvolutionFWD, Conv3DNCDHW)
|
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TEST(ReferenceConvolutionFWD, Conv3DGNCDHW)
|
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{
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ck::utils::conv::ConvParams params;
|
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params.num_dim_spatial_ = 3;
|
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params.N_ = 1;
|
||||
params.K_ = 1;
|
||||
params.C_ = 2;
|
||||
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3, 3};
|
||||
params.input_spatial_lengths_ = std::vector<ck::index_t>{6, 6, 6};
|
||||
params.conv_filter_strides_ = std::vector<ck::index_t>{1, 1, 1};
|
||||
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
|
||||
params.input_left_pads_ = std::vector<ck::index_t>{0, 0, 0};
|
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params.input_right_pads_ = std::vector<ck::index_t>{0, 0, 0};
|
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ck::utils::conv::ConvParam conv_param(3,
|
||||
1,
|
||||
1,
|
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1,
|
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2,
|
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std::vector<ck::index_t>{3, 3, 3},
|
||||
std::vector<ck::index_t>{6, 6, 6},
|
||||
std::vector<ck::index_t>{1, 1, 1},
|
||||
std::vector<ck::index_t>{1, 1, 1},
|
||||
std::vector<ck::index_t>{0, 0, 0},
|
||||
std::vector<ck::index_t>{0, 0, 0});
|
||||
|
||||
auto out_tensor = run_reference_convolution_forward<3,
|
||||
float,
|
||||
float,
|
||||
float,
|
||||
ck::tensor_layout::convolution::NCDHW,
|
||||
ck::tensor_layout::convolution::KCZYX,
|
||||
ck::tensor_layout::convolution::NKDHW>(
|
||||
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
|
||||
std::vector<std::size_t> ref_dims{1, 1, 4, 4, 4};
|
||||
ck::tensor_layout::convolution::GNCDHW,
|
||||
ck::tensor_layout::convolution::GKCZYX,
|
||||
ck::tensor_layout::convolution::GNKDHW>(
|
||||
conv_param, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
|
||||
std::vector<std::size_t> ref_dims{1, 1, 1, 4, 4, 4};
|
||||
std::vector<float> ref_data{
|
||||
407.7, 410.40002, 413.09998, 415.80002, 423.90002, 426.6, 429.30002, 432.,
|
||||
440.1, 442.80002, 445.5, 448.2, 456.30002, 459., 461.7, 464.40002,
|
||||
@@ -344,29 +344,29 @@ TEST(ReferenceConvolutionFWD, Conv3DNCDHW)
|
||||
ck::utils::check_err(out_tensor.mData, ref_data, "Error [case 1]: incorrect results!"));
|
||||
}
|
||||
|
||||
TEST(ReferenceConvolutionFWD, Conv3DNCDHWStridesDilations)
|
||||
TEST(ReferenceConvolutionFWD, Conv3DGNCDHWStridesDilations)
|
||||
{
|
||||
ck::utils::conv::ConvParams params;
|
||||
params.num_dim_spatial_ = 3;
|
||||
params.N_ = 1;
|
||||
params.K_ = 2;
|
||||
params.C_ = 2;
|
||||
params.filter_spatial_lengths_ = std::vector<ck::index_t>{3, 3, 3};
|
||||
params.input_spatial_lengths_ = std::vector<ck::index_t>{12, 12, 12};
|
||||
params.conv_filter_strides_ = std::vector<ck::index_t>{3, 3, 3};
|
||||
params.conv_filter_dilations_ = std::vector<ck::index_t>{1, 1, 1};
|
||||
params.input_left_pads_ = std::vector<ck::index_t>{0, 0, 0};
|
||||
params.input_right_pads_ = std::vector<ck::index_t>{0, 0, 0};
|
||||
ck::utils::conv::ConvParam conv_param(3,
|
||||
1,
|
||||
1,
|
||||
2,
|
||||
2,
|
||||
std::vector<ck::index_t>{3, 3, 3},
|
||||
std::vector<ck::index_t>{12, 12, 12},
|
||||
std::vector<ck::index_t>{3, 3, 3},
|
||||
std::vector<ck::index_t>{1, 1, 1},
|
||||
std::vector<ck::index_t>{0, 0, 0},
|
||||
std::vector<ck::index_t>{0, 0, 0});
|
||||
|
||||
auto out_tensor = run_reference_convolution_forward<3,
|
||||
float,
|
||||
float,
|
||||
float,
|
||||
ck::tensor_layout::convolution::NCDHW,
|
||||
ck::tensor_layout::convolution::KCZYX,
|
||||
ck::tensor_layout::convolution::NKDHW>(
|
||||
params, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
|
||||
std::vector<std::size_t> ref_dims{1, 2, 4, 4, 4};
|
||||
ck::tensor_layout::convolution::GNCDHW,
|
||||
ck::tensor_layout::convolution::GKCZYX,
|
||||
ck::tensor_layout::convolution::GNKDHW>(
|
||||
conv_param, ck::utils::FillMonotonicSeq<float>{0.f, 0.1f});
|
||||
std::vector<std::size_t> ref_dims{1, 1, 2, 4, 4, 4};
|
||||
std::vector<float> ref_data{
|
||||
2756.7002, 2764.7998, 2772.9001, 2781., 2853.9001, 2862., 2870.1, 2878.2002,
|
||||
2951.1, 2959.2002, 2967.2998, 2975.4001, 3048.2998, 3056.4001, 3064.5, 3072.6,
|
||||
|
||||
Reference in New Issue
Block a user