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https://github.com/ROCm/composable_kernel.git
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5d37d7bff4
* delete obselete files * move files * build * update cmake * update cmake * fix build * reorg examples * update cmake for example and test
263 lines
12 KiB
C++
263 lines
12 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 "config.hpp"
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#include "conv_utils.hpp"
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#include "device.hpp"
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#include "device_tensor.hpp"
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#include "device_convnd_fwd_xdl_nhwc_kyxc_nhwk.hpp"
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#include "element_wise_operation.hpp"
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#include "host_tensor.hpp"
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#include "reference_conv_fwd.hpp"
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#include "tensor_layout.hpp"
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#include "test_util.hpp"
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namespace {
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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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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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static constexpr auto ConvFwdDefault =
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ck::tensor_operation::device::ConvolutionForwardSpecialization_t::Default;
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template <ck::index_t SpatialDims, typename InDataType, typename WeiDataType, typename OutDataType>
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using DeviceConvNDFwdInstance = ck::tensor_operation::device::
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DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<
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// clang-format off
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InDataType, //
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WeiDataType, //
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OutDataType, //
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InDataType, //
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InElementOp, // Input Elementwise Operation
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WeiElementOp, // Weights Elementwise Operation
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OutElementOp, // Output Elementwise Operation
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ConvFwdDefault, // ConvForwardSpecialization
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SpatialDims, // SptialDims
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64, // BlockSize
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16, // MPerBlock
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16, // NPerBlock
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4, // K0PerBlock
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1, // K1
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16, // MPerXDL
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16, // NPerXDL
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1, // MXdlPerWave
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1, // NXdlPerWave
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S<1, 16, 1>, // ABlockTransferThreadClusterLengths_K0_M_K1
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S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
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S<1, 0, 2>, // ABlockTransferSrcAccessOrder
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2, // ABlockTransferSrcVectorDim
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1, // ABlockTransferSrcScalarPerVector
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1, // ABlockTransferDstScalarPerVector_K1
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true, // ABlockLdsAddExtraM
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S<1, 16, 1>, // BBlockTransferThreadClusterLengths_K0_N_K1
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S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
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S<1, 0, 2>, // BBlockTransferSrcAccessOrder
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2, // BBlockTransferSrcVectorDim
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1, // BBlockTransferSrcScalarPerVector
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1, // BBlockTransferDstScalarPerVector_K1
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true, // BBlockTransferAddExtraN
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7, // CThreadTransferSrcDstVectorDim
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1>; // CThreadTransferDstScalarPerVector
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// clang-format on
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template <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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auto GetHostTensors(const ck::conv_util::ConvParams& params)
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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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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 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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Tensor<InDataType> input(ck::conv_util::GetHostTensorDescriptor(input_dims, InLayout{}));
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Tensor<WeiDataType> weights(ck::conv_util::GetHostTensorDescriptor(filter_dims, WeiLayout{}));
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Tensor<OutDataType> host_output(
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ck::conv_util::GetHostTensorDescriptor(output_dims, OutLayout{}));
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Tensor<OutDataType> device_output(
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ck::conv_util::GetHostTensorDescriptor(output_dims, OutLayout{}));
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std::generate(input.begin(), input.end(), [n = 0]() mutable {
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return InDataType(n++) * InDataType(0.1f);
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});
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std::fill(weights.begin(), weights.end(), WeiDataType(0.5f));
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std::fill(host_output.begin(), host_output.end(), OutDataType(0.f));
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std::fill(device_output.begin(), device_output.end(), OutDataType(0.f));
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return std::make_tuple(input, weights, host_output, device_output);
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}
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template <ck::index_t NDim,
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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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void RunReferenceConv(const ck::conv_util::ConvParams& params,
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const Tensor<InDataType>& input,
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const Tensor<WeiDataType>& weights,
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Tensor<OutDataType>& output)
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{
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auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<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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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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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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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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ref_invoker.Run(ref_argument);
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}
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template <ck::index_t NDim,
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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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void RunConv(const ck::conv_util::ConvParams& params,
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const Tensor<InDataType>& input,
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const Tensor<WeiDataType>& weights,
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Tensor<OutDataType>& output)
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{
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DeviceMem in_device_buf(sizeof(InDataType) * input.mDesc.GetElementSpace());
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DeviceMem wei_device_buf(sizeof(WeiDataType) * weights.mDesc.GetElementSpace());
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DeviceMem out_device_buf(sizeof(OutDataType) * output.mDesc.GetElementSpace());
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in_device_buf.ToDevice(input.mData.data());
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wei_device_buf.ToDevice(weights.mData.data());
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const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
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auto conv = DeviceConvNDFwdInstance<NDim, InDataType, WeiDataType, OutDataType>();
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auto invoker = conv.MakeInvoker();
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auto argument = conv.MakeArgument(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
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static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
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static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
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params.N,
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params.K,
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params.C,
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params.input_spatial_lengths,
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params.filter_spatial_lengths,
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output_spatial_lengths,
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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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InElementOp{},
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WeiElementOp{},
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OutElementOp{});
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if(!conv.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"Error! device_conv with the specified compilation parameters does "
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"not support this Conv problem");
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}
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invoker.Run(argument);
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out_device_buf.FromDevice(output.mData.data());
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}
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bool TestConv2DNHWC()
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{
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bool res{true};
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ck::conv_util::ConvParams params;
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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.input_spatial_lengths = std::vector<ck::index_t>{16, 16};
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params.conv_filter_strides = std::vector<ck::index_t>{1, 1};
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auto host_tensors = GetHostTensors(params);
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const Tensor<float>& input = std::get<0>(host_tensors);
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const Tensor<float>& weights = std::get<1>(host_tensors);
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Tensor<float>& host_output = std::get<2>(host_tensors);
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Tensor<float>& device_output = std::get<3>(host_tensors);
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RunReferenceConv<2>(params, input, weights, host_output);
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RunConv<2>(params, input, weights, device_output);
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res = res &&
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test_util::check_err(
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device_output.mData, host_output.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
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return res;
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}
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bool TestConv1DNWC()
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{
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bool res{true};
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ck::conv_util::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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auto host_tensors = GetHostTensors<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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const Tensor<float>& input = std::get<0>(host_tensors);
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const Tensor<float>& weights = std::get<1>(host_tensors);
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Tensor<float>& host_output = std::get<2>(host_tensors);
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Tensor<float>& device_output = std::get<3>(host_tensors);
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RunReferenceConv<1>(params, input, weights, host_output);
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RunConv<1>(params, input, weights, device_output);
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res = res &&
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test_util::check_err(
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device_output.mData, host_output.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
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return res;
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}
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} // anonymous namespace
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int main()
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{
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bool res{true};
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res = TestConv1DNWC();
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std::cout << "TestConv1DNWC ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
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res = TestConv2DNHWC();
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std::cout << "TestConv2DNHWC ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
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}
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